cv: name: Daniel Fridljand photo: ../images/fridljand_daniel.jpg location: Munich, Germany website: https://danielfridljand.de social_networks: - network: LinkedIn username: daniel-fridljand - network: GitHub username: FridljDa sections: summary: - "I am a driven data scientist with a strong academic background in mathematics, statistics, and bioinformatics. My passion for machine learning, software development, and coding has led me to work on projects across diverse domains, including public health, genetics, and oncology. With three years of scientific software development experience and a first-author publication in a high-impact journal, I'm committed to leveraging computational skills to solve real-world challenges." software_development_experience: - company: TNG Technology Consulting position: Software Consultant - Applied AI location: Munich, Germany start_date: 2025-12 end_date: present highlights: - "**Project: AI-Powered Customer Support Automation** — *cinema-ticketing SaaS, live in production*" - "**Sole AI engineer** owning end-to-end delivery — from initial customer discovery and architecture through to live production rollout in April 2026; full ownership from data engineering and intent classification to resolution execution and live observability." - "**Live in production**: 175 unique B2C support tickets processed during a two-week live observation window (21 Apr – 4 May 2026), reviewed and annotated by 9 customer-side support staff plus the TNG implementation lead. **Approval rate 72.9% strict / 81.3% content-supported** on model-relevant tickets (n=144); **79.6% / 88.6%** on tickets where the decisive context is fully accessible to the system." - "Customer-trust signals during rollout: daily ticket volume scaled 5x (4–7 → 14–36 tickets/workday) following internal CEO showcase, with approval mix stable across the volume increase; operator-error rate fell from ~25% to <10% as staff converged on correct tool usage; reviewer free-text engagement grew from 58 comments in the 6 weeks pre-rollout to 159 in the following 11 days, concentrated on non-approved tickets — providing the structured improvement signal driving subsequent iteration." - "**AI/LLM Solution Architecture**" - "Deliberately chose a deterministic workflow architecture over a free-form agent: analysis of 1,000+ historical tickets showed >80% of customer requests mapped to one of 19 predefined customer-intent categories, making behaviour predictable and post-feedback fixes reliably localised to specific code paths — the right trade-off for a constrained, recurring business domain." - "Engineered hybrid solution-method architecture supporting both deterministic (hardcoded) and agentic (LLM-powered) resolution strategies, with abstract base class and specialised subclasses enabling flexible per-intent automation approaches." - "Designed two-phase agentic workflow: LLM-powered data fetching with dynamic API endpoint selection, followed by multi-step plan generation with conditional branching — solving multi-transaction disambiguation and enabling multilingual (German/English) support." - "Designed 19 distinct customer-intent categories with disambiguation logic and edge-case handling, with deterministic routing based on classification confidence and information completeness; categorised intents by automation feasibility (Automatable vs. manual escalation) with comprehensive markdown documentation containing Mermaid flowcharts and API endpoint mappings per intent." - "Implemented scatter-gather pattern for tool execution: resolution plans contain ordered tool calls (up to 9 steps) executed sequentially with error handling, enabling complex multi-step automations (e.g., fetch transaction → resend ticket → send confirmation → close ticket)." - "Applied DSPy with Genetic-Pareto algorithm (GEPA) for automated LLM prompt optimisation, demonstrating knowledge of advanced prompt engineering and metaheuristic optimisation techniques." - "Created 77 `.prompty` template files following Microsoft Prompty specification for modular, version-controlled prompt management across intent classification, information extraction, and intent-specific resolution workflows." - "**Workflow Orchestration & Automation**" - "Implemented durable workflow orchestration using **Temporal** with 5-stage pipeline (intent classification, information extraction, resolution dispatch, human approval via signals, tool execution), ensuring reliable execution with automatic retries and deterministic replay." - "Designed human-in-the-loop approval mechanism using Temporal Signals for staff review before automated resolution execution, with workflow state queries for real-time progress monitoring via Temporal Web UI." - "**Observability, Evaluation & Testing**" - "**Integrated Langfuse observability platform** for comprehensive AI monitoring: automatic trace generation for every request with hierarchical spans, real-time latency tracking, token usage analytics, and production debugging capabilities with full input/output capture." - "Built production-grade evaluation framework with **6 evaluation suites** (intent classification, information extraction, end-to-end, fetch_data, plan_generation, regression) using Langfuse dataset runs for longitudinal performance tracking and regression detection." - "Implemented multi-dimensional scoring system with **8 score types** including critical scores (`correct_tools_selected`, `fetch_tools_correct`), LLM-as-judge semantic evaluation, and automated run-level metrics (accuracy, precision, recall, F1) posted to Langfuse for cross-run comparison." - "Designed and shipped a **14-label outcome taxonomy** (e.g., `Approved`, `Wording discrepancies`, `Critical context only in Zendesk`, `Critical information is in Unzer`, `Staff found ticket, KI could not`) plus a Streamlit analytics dashboard exposing per-intent, per-reviewer, and per-day breakdowns of live performance — the operational substrate for live evaluation, root-cause analysis, and stakeholder communication." - "Surfaced and quantified inter-rater variance across the customer's reviewer population (37–100% per-reviewer approval rate on comparable ticket mix) and proposed annotator-calibration mechanisms — an annotator briefing with worked examples per outcome label, plus 10–15% spot-check re-annotation by a senior reviewer — as a precondition for broader scaling." - "Built comprehensive test suite with **97 test files** across 4 test types: unit (business logic), integration (service contracts), end-to-end (full workflow with mock servers), and evaluation (AI performance metrics tracked in Langfuse)." - "Designed evaluation isolation strategy: separate evaluations for fetch logic vs. plan generation using direct method calls with controlled inputs, enabling precise failure diagnosis and faster iteration cycles." - "Implemented mock server infrastructure for Client API and Zendesk APIs with stateful test fixtures, enabling deterministic integration testing and controlled failure scenario simulation." - "**Live Iteration & Continuous Improvement**" - "Operated a tight feedback loop with the customer's support team: 41 tickets re-submitted after a code fix during the live observation window (~3 fix-cycles per workday, ~1–2 h of engineering each), with 52% approved on the second pass — demonstrating that reviewer signal translates reliably into shippable code changes." - "Drove 13+ feature improvements directly from live feedback: new spam-handling intent (polite, non-escalating responses), expanded refund scenarios beyond double-charge, reservation-vs-purchase disambiguation, sequential (rather than batched) information solicitation from customers, HTML-content rendering, image-attachment handling, and runtime-configurable intents to handle outages without redeployment." - "Designed a **self-improvement harness** as the next-generation iteration mechanism: convert reviewer free-text feedback directly into eval-gated pull requests for human approval — addressing the engineering-translation step as the marginal scaling bottleneck once reviewer-side calibration is in place." - "**REST API & Hexagonal Architecture**" - "Independently designed and built 20+ REST endpoints following OpenAPI 3.1 specification with clear separation of concerns (`/extract/*` for queries, `/tool/*` for actions, `/workflow/*` for orchestration), demonstrating API design best practices." - "Implemented Hexagonal Architecture (Ports & Adapters pattern) ensuring complete separation between business logic and infrastructure, with explicit input/output ports and pluggable secondary adapters for external systems (LLM, monitoring, Client API)." - "**Data Engineering & Pipeline Architecture (Bridging Raw Data to Clean Datasets)**" - "Architected reproducible 22-stage Snakemake data pipeline processing 1,000+ historical tickets through automated stages (filtering, PII censoring, LLM labeling, balanced sampling), with scatter-gather parallelism for efficiency (500-ticket chunks), checkpoint-based incremental execution, and declarative YAML configuration — demonstrating infrastructure-as-code and scientific computing best practices." - "Implemented multilingual PII detection system using Microsoft Presidio with dual spaCy models (English `en_core_web_lg`, German `de_core_news_lg`) and Faker for realistic synthetic data replacement, ensuring GDPR compliance while maintaining data utility for model training with configurable entity detection (emails, phone numbers, IBAN, credit cards) and custom deny-lists for false-positive suppression." - "Engineered balanced dataset sampling strategy with configurable samples-per-intent parameter (default 10), filtering non-actionable intents (AccidentalContact, ConversationCutoff), and automated truncation of agent resolutions to create Evaluation Datasets for benchmarking and iteratively optimising agent performance — preventing class imbalance and improving model fairness." - "Built custom DSPy-compatible batch LM adapter for TNG API with asynchronous batch processing, automatic request chunking, exponential backoff polling, and comprehensive error handling — reducing API costs and enabling efficient classification of large ticket datasets." - "Implemented sophisticated author role classification using LLM-powered analysis to disambiguate unknown comment authors (customer vs. agent) in conversation threads, with batch processing and context-aware prompting — improving conversation understanding and intent classification accuracy." - "Designed dual-path data architecture maintaining separate censored and uncensored pipelines from extraction through final outputs, enabling privacy-compliant LLM processing while preserving full context for internal debugging and visualisation tools." - "Implemented scatter-gather parallelism in Snakemake for PII censoring (500-ticket chunks), author classification, and LLM labeling stages — enabling horizontal scalability and fault isolation with automatic chunk-level caching via Snakemake checkpoints." - "Developed multi-view Streamlit data labelling application with ticket browsing, CSV-backed manual labelling persistence with timestamp tracking, real-time filtering across the historical ticket corpus, automatic label merging (manual overrides LLM), JavaScript-based deep linking (`?ticket=ID`) for reproducible data reviews, summary statistics with intent distribution visualisation, and raw JSON inspection — accelerating dataset annotation and quality assurance workflows." - "Generated automated confusion matrices and distribution histograms (label, channel, satisfaction) from labeled datasets with intent-specific colour mapping and visualisation, enabling longitudinal model performance tracking and data quality monitoring." - "Created comprehensive data exploration workflow with 4 Jupyter notebooks for initial EDA (ticket ID patterns, author comment analysis, label distribution, channel analysis) and 1 Python script for endpoint priority analysis, informing feature engineering decisions and automation strategy." - "Established Git as single source of truth for evaluation datasets (JSONL format) with automated Langfuse synchronisation via registration scripts, enabling reproducible evaluation runs and dataset versioning." - "Documented open architectural questions in structured markdown (`OPEN_QUESTIONS.md`) with problem descriptions, multiple solution options with pros/cons, API endpoint requirements, and stakeholder decision points — demonstrating requirements gathering, trade-off analysis, and technical communication skills." - "**Developer Experience & AI-Assisted Development**" - "Pioneered AI-assisted spec-driven development workflow using Cursor IDE with custom constitution and templates: 20+ feature specs (001-021) following standardised template structure (user stories with priorities, acceptance scenarios, functional requirements, success criteria), version-controlled project constitution (v1.17.0) with 19 principles governing architecture and development practices, bash automation scripts for feature lifecycle management (`create-new-feature.sh`, `setup-plan.sh`, `update-agent-context.sh`), and systematic feature progression from technology-agnostic specifications through implementation planning to task breakdown — enabling consistent architecture decisions, rapid iteration, and seamless collaboration between human developer and AI coding assistant while maintaining Hexagonal Architecture compliance and evaluation-driven quality standards." - "Built **3 custom Cursor Agent Skills**: Langfuse API integration for trace analysis, OpenAPI specification comparison for API evolution tracking, and uv package manager workflows." - "**Developed custom Cursor Agent Skills** for automated system improvement: Langfuse API skill enables programmatic trace analysis, failure triage (eval data vs. mock server vs. prompts), and iterative prompt refinement based on production observability data." - "Created interactive **Chainlit web interface** for real-time conversation testing and resolution plan inspection, accelerating development feedback loops." - "**Tech Stack**: FastAPI (REST API), Streamlit (data labelling & analytics), Chainlit (chat interface), Python 3.13+, PydanticAI (agent framework), Temporal (workflow orchestration), Langfuse (observability), Snakemake (data pipeline), Microsoft Presidio (PII detection), Docker, pytest (testing), DSPy with GEPA (prompt optimisation), Microsoft Prompty (prompt templates), MCP (tool adapters)" - "**Project: LLM-Powered Document Validation System**" - "Core developer in a 3-person team for production-grade automated proposal review service, co-developing complex .docx document processing with automated comment insertion" - "Architected evaluation framework with automated correction classification system (TP/FP/FN/unknown positives) using Pydantic models, regex/token-based pattern matching, whitespace normalization, and precision/recall/F1 metrics" - "Integrated Langfuse observability platform with conditional tracing (@observe decorators, profile-based toggling), auto-dataset registration, batch evaluation orchestration, and Grafana dashboards for metric visualization" - "Developed two Cursor agent skills (800+ lines docs, 3 Python scripts) enabling AI-assisted iterative improvement of evaluation data by analyzing Langfuse traces and generating CorrectionPattern suggestions" - "Migrated evaluation data to structured JSONL format, prompts to .prompty files, implemented DOCX comment extractor, and established GitOps workflow for Docker Compose stack with Grafana/Langfuse/ClickHouse" - company: TNG Technology Consulting position: Software Consultant - Enterprise Modernization location: Munich, Germany start_date: 2024-12 end_date: 2025-12 highlights: - "**Platform Modernization in Supply Chain Software**: Member of the platform team modernizing a mission-critical supply chain management application in a multi-year transformation program (Java 8 to 17, JBoss to WildFly)." - "**DevOps Initiative with High ROI**: Implemented a JFrog Artifactory proxy in 3 days after repeated postponement due to overestimated effort. The solution has been continuously used since rollout and saves developers approximately 1-2 hours per person per week." - "**DevSecOps & Security Governance**: Designed and configured OWASP scans in the CI pipeline and established CVE visibility for stakeholders, enabling proactive risk management throughout modernization." - "**Endpoint Modernization**: Migrated internal virus scanning service from SOAP endpoints to REST endpoints with Keycloak authentication, onboarding existing clients to the modernized API." - "**Technical Debt & Quality**: Systematically reduced legacy debt through aggressive quality governance, implementing static analysis and automated testing to establish a foundation for cloud-native migration." - "**SAFe Delivery in Distributed Teams**: Delivered features in multinational distributed Scrum teams (5-8 engineers) under SAFe, contributed to Program Increment (PI) planning, and supported reliable two-week sprint releases." - "in charge of mantaining and upgrading ELK. ELK was used to monitor logs from servers and have text serch fields within the application" - "I build Graphana dashboards to visualize proxy metrics for modernization. for example, number of CVEs per application and severity over time. persisted in Prometheus." - "**Cross-Team Synergies**: Created synergies between feature teams working on different applications, improving collaboration and knowledge sharing." - "**Internal Knowledge Sharing**: Co-authored the internal 'AI Tool of the Week' blog series, providing weekly summaries of AI advancements including agentic workflows, AI-assisted coding, and multi-modal processing." - "**Tech Stack**: TypeScript, Java 17, Spring Boot, Oracle DB, OpenRewrite, Gradle, JUnit, Docker/Podman, Jenkins, SonarQube, OWASP Dependency-Check, JFrog Artifactory." data_science_experience: - company: ETH Zürich - Department of Biosystems Science and Engineering position: Research Data Analyst - Computational Oncology location: Basel, Switzerland start_date: 2024-02 end_date: 2024-09 highlights: - "Developed novel statistical methods for estimating mutational signatures in cancer genomes and analyzed single-cell DNA sequencing data from the Tumor Profiler Study" - "**Advanced Bayesian Modeling for Cancer Evolution**: Developed novel statistical methods for estimating mutational signatures in cancer genomes under guidance of Prof. Niko Beerenwinkel" - "**Hierarchical Dirichlet Process (HDP) Innovation**: Extended Bayesian non-parametric HDP model to incorporate phylogenetic tree structures, enabling learning of evolutionary dependencies between cancer subclones" - "**Mathematical Framework**: Implemented hierarchical dependency structure where random measure (distribution of signatures) for child node is drawn from base distribution defined by parent, mathematically enforcing biological intuition of inheritance" - "**Non-Parametric Bayesian Inference**: Utilized Dirichlet Process (DP) as prior in infinite mixture models, allowing model to learn number of mutational signatures from data rather than fixing a priori" - "**Implementation in R**: Developed prototype using libraries for Dirichlet processes and custom tree-traversal algorithms for phylogenetic structure integration" - "**Signature Discovery**: Successfully identified eight latent mutational signatures, demonstrating model's capability to capture cancer evolutionary processes" - "**Critical Evaluation**: Identified limitations when comparing with COSMIC database (low cosine similarity), revealing that global databases derived from bulk sequencing may not be directly applicable to sparse single-cell data without informative priors" - "**Theoretical Contribution**: Bridged gap between standard Non-negative Matrix Factorization (NMF) approaches treating tumors as 'bags of mutations' and evolutionary-aware signature estimation" - "**Teaching Experience**: Delivered lecture on Statistical Models in Computational Biology covering hidden Markov models, EM algorithm, and Variational inference" - "**Deep Mathematical Maturity**: Demonstrated expertise in stochastic processes, Bayesian non-parametrics, phylogenetics, and computational oncology" - company: Stanford University School of Medicine position: Research Data Analyst - Environmental Epidemiology location: Palo Alto, USA start_date: 2023-07 end_date: 2023-12 highlights: - "Devised and implemented the statistical analysis in R, synthesized findings from 150 pertinent publications, and drove the manuscript from conceptualization to successful publication in Nature Medicine" - "**Nature Medicine First Co-Author**: Led end-to-end statistical analysis resulting in first-author publication in Nature Medicine (2024), one of the world's top medical journals" - "**Causal Inference in Environmental Health**: Quantified specific contribution of air pollution (PM2.5) to mortality gap between racial and socioeconomic groups in United States, disentangling effects from confounders" - "**Big Data Engineering**: Curated and harmonized heterogeneous datasets including satellite-derived pollution estimates, census demographics, and death records across entire US (2000-2016)" - "**Multi-Scale Geographic Data Integration**: Harmonized three distinct geographic resolutions (0.01° × 0.01° satellite grid → census tract level → county level) requiring spatial interpolation and population-weighted aggregation algorithms" - "**Massive-Scale ETL Pipeline**: Processed 63+ million death records spanning 16 years (1990-2016) across 3,000+ US counties, integrating data from US Census Bureau, CDC National Vital Statistics, EPA air quality monitors, and satellite remote sensing" - "**Temporal Data Harmonization**: Implemented linear interpolation algorithms to bridge census boundary changes across three decades (1990, 2000, 2010 vintages) using Longitudinal Tract Database crosswalks" - "**GitHub Repository**: Published complete reproducible analysis pipeline at https://github.com/FridljDa/pm25_inequality demonstrating software engineering best practices" - "**Data Publication**: Deposited national-level estimates in Zenodo (https://doi.org/10.5281/zenodo.11243236) following FAIR data principles" - "**Statistical Modeling Expertise**: Implemented causal inference models in R to estimate 'Attributable Fraction' of mortality, adjusting for confounders using multivariate regression and sensitivity analyses" - "**Interactive R Shiny Application**: Developed custom web application as analytical engine (not just presentation tool) allowing co-authors to explore 17-dimensional data interactively, visualizing non-linear relationships and detecting outliers" - "**High-Impact Finding**: Revealed that over 50% of age-adjusted all-cause mortality difference between Black and White populations in US is attributable to environmental factors, with profound policy implications" - "**Rigorous Peer Review Navigation**: Led manuscript through competitive 'Revise and Resubmit' cycle with strict two-month deadline, generating 15 new complex figures and additional robusticity checks to satisfy reviewers" - "**Scientific Synthesis**: Synthesized findings from over 150 pertinent publications to ground statistical results in broader epidemiological context, demonstrating strong scientific communication skills" - "**Manuscript Ownership**: Drove project from conceptualization to successful publication, writing initial manuscript and executing major revisions under tight deadlines" - "**Large-Scale Data Analysis**: Processed county-level data covering 3,000+ US counties over 16 years, demonstrating capability to handle massive public health datasets" - "**Collaboration with Domain Experts**: Worked closely with Pascal Geldsetzer and interdisciplinary team of epidemiologists, environmental scientists, and statisticians" - company: European Molecular Biology Laboratory (EMBL) position: Research Data Analyst - Statistical Genomics (Master's Thesis) location: Heidelberg, Germany start_date: 2021-10 end_date: 2022-05 highlights: - "Developed and implemented a novel statistical method in R and C++ to identify outliers in large-scale genomic datasets, increasing discovery power by over 30%" - "**IHW-Forest Algorithm Development**: Developed novel statistical procedure addressing multiple testing crisis in modern genomics, where millions of simultaneous hypothesis tests create false discovery challenges" - "**Machine Learning for Hypothesis Weighting**: Designed Independent Hypothesis Weighting with Random Forests, leveraging multivariate covariates to increase statistical power beyond traditional Bonferroni and Benjamini-Hochberg corrections" - "**Mechanism Innovation**: Random Forest regressor predicts probability of hypothesis being true based on metadata (gene expression, conservation score), with leaves partitioning covariate space for optimal weighting" - "**False Discovery Rate (FDR) Control**: Constructed weights averaging to 1, ensuring overall FDR control while maximizing power for promising hypotheses in high-probability leaves" - "**C++ Performance Optimization**: Core splitting and weighting logic optimized using C++ (via Rcpp) for handling sheer volume of genomic data, bridging high-level statistical abstraction with low-level memory management" - "**Massive-Scale Validation**: Applied to dataset of 16 billion genetic association tests, increasing number of discovered associations by >30% compared to standard methods" - "**Curse of Dimensionality Solution**: Solved key limitation where standard IHW fails with high-dimensional covariates, demonstrating algorithmic innovation for big data" - "**Seven Scientific Presentations**: Presented research at seven events including seminar talks at Yale University and University of North Carolina, and competitively selected oral contribution at DAGStat 2022 (100+ scholars)" - "**Peer Review Service**: Conducted peer reviews for manuscripts at Bioinformatics Advances and Cell Biology, demonstrating scientific maturity" - "**Supervision**: Worked under guidance of Prof. Jan Johannes (Heidelberg), Dr. Wolfgang Huber (EMBL), and Dr. Nikos Ignatiadis (EMBL), senior leaders in statistical methodology" - "**Master's Thesis Grade**: 1.0 (highest distinction) for thesis 'Better multiple Testing: Using multivariate co-data for hypothesis weighting'" - company: Heidelberg Institute of Global Health position: Research Data Analyst - Environmental Epidemiology location: Heidelberg, Germany start_date: 2020-10 end_date: 2021-09 highlights: - "**Early-Stage Air Pollution Research**: Initial analysis phase of air pollution and health inequalities project in US under Pascal Geldsetzer's guidance, laying groundwork for subsequent Stanford collaboration" - "**Data Wrangling and Exploration**: Preliminary data harmonization of air quality, mortality, and demographic datasets to establish feasibility of large-scale causal inference analysis" - "**Research Continuity**: Project continued at Stanford (July 2023 - Dec 2023), ultimately resulting in Nature Medicine publication demonstrating long-term research commitment" education: - institution: University of Heidelberg area: Mathematics degree: M.Sc. location: Heidelberg, Germany start_date: 2020-10 end_date: 2023-05 highlights: - "Grade: 1.0 (full marks, highest distinction)" - "Master's Thesis: 'Better multiple Testing: Using multivariate co-data for hypothesis weighting' (Supervisors: Prof. Jan Johannes, Dr. Wolfgang Huber, EMBL)" - "Focus Areas: Probability Theory, Machine Learning, High-Dimensional Statistics, Selective Inference, Stochastic Processes" - "Selected Coursework: SQL and Database Systems, Statistics for Machine Learning, Graphical Modelling, Random Forests, Differential Geometry" - "Awards: Gerhard C. Starck Foundation Stipend (merit-based), Baden-Württemberg Stipend (excellence award)" - "Research Integration: Thesis work conducted at EMBL, integrating academic rigor with world-class research environment" - institution: Yale University area: Applied Mathematics degree: Exchange Scholar location: New Haven, USA start_date: 2022-08 end_date: 2023-05 highlights: - "Prestigious Selection: Chosen as one of two university-wide representatives from University of Heidelberg for year-long exchange program" - "Grade: Honors (full marks) - highest academic distinction at Yale" - "Program Affiliation: Hosted by Applied Mathematics Program, advised by Prof. Smita Krishnaswamy" - "Advanced Coursework: 'Theory and Application of Deep Learning' (cutting-edge neural network theory), 'Geometric & Topological Methods in Machine Learning' (Prof. Smita Krishnaswamy - manifold learning, TDA), 'Differentiable Manifolds' (mathematical foundations), 'Statistical Methods in Human Genetics'" - "Research Presentation: Presented at Yale Applied Math Seminar on 'Independent Hypothesis Weighting' to audience of faculty and graduate students" - "Award: German Academic Exchange Service (DAAD) Stipend for academic excellence and research potential" - "Academic Integration: Engaged with Yale's top-tier machine learning and applied mathematics community, attending seminars and collaborating with researchers" - "Cross-Disciplinary Exposure: Coursework spanning pure mathematics (differential geometry) to applied ML (deep learning), demonstrating theoretical depth and practical orientation" - institution: University of Heidelberg area: Mathematics degree: B.Sc. location: Heidelberg, Germany start_date: 2017-10 end_date: 2020-09 highlights: - "Grade: 1.4 (excellent, top 10% of cohort)" - "Bachelor's Thesis: 'Online estimation of the geometric median in a Hilbert space' - focusing on stochastic gradient descent convergence rates in infinite-dimensional spaces" - "Minor: Computer Science" - "Foundation: Rigorous training in real analysis, linear algebra, abstract algebra, topology, probability theory, and numerical analysis" - "Award: Gerhard C. Starck Foundation Stipend for outstanding academic performance" - "Early Research Exposure: Thesis work on optimization in functional analysis setting, demonstrating interest in theoretical foundations of machine learning" - institution: Hebrew University of Jerusalem area: Mathematics degree: Exchange Student location: Jerusalem, Israel start_date: 2019-09 end_date: 2020-03 highlights: - "International Academic Experience: Completed semester abroad at Israel's premier research university during undergraduate studies" - "Graduate-Level Coursework: Functional Analysis, Algebraic Combinatorics, and Quantitative Models at the Einstein Institute of Mathematics, broadening mathematical perspective through different pedagogical tradition" - "Awards: PROMOS Stipend (DAAD mobility program), Hebrew University of Jerusalem Stipend for academic merit" - "Cultural Immersion: Developed cross-cultural communication skills and adaptability in diverse academic environment" - institution: Karl-Friedrich-Gymnasium Mannheim area: General Education degree: Abitur (German University Entrance Qualification) location: Mannheim, Germany start_date: 2009-09 end_date: 2017-06 highlights: - "Grade: 1.0 (full marks)" certifications: - name: Celonis Foundations date: 2026-02 url: https://www.credly.com/badges/5c45662f-40e2-48fa-9052-9b516020d810 - name: Neural Networks and Deep Learning - name: Certified SAFe 6 Practitioner - name: SAFe for Teams Course (6.0) - name: Toefl iBT 110 - name: Machine Learning in Production teaching_experience: - company: Studybees GmbH position: Crash Course Tutor - Mathematics, Computer Science & Economics location: Germany start_date: 2018-04 end_date: 2019-08 highlights: - "**Large-Scale Mentorship**: Mentored over 150 university students at University of Mannheim across 10 intensive crash courses for high-attrition subjects" - company: Springer Nature position: Freelance Writer - Mathematical Assessment Development location: Germany start_date: 2019-08 end_date: 2019-08 highlights: - "**Specialized Content Creation**: Developed two comprehensive mathematical exams focused on statistical applications in laboratory settings for molecular biology students" - "**Interdisciplinary Expertise**: Designed assessments requiring fusion of mathematical precision with biological context, testing analytical capabilities at intersection of quantitative and life sciences" - "**Publisher Collaboration**: Worked with Springer Nature, world's leading academic publisher, ensuring content met rigorous quality and pedagogical standards" - "**Assessment Design**: Balanced theoretical understanding with practical application, creating problems testing both computational skills and conceptual comprehension" publications: - title: "Disparities in air pollution attributable mortality in the US population by race, ethnicity and sociodemographic factors" authors: - Pascal Geldsetzer - "***Daniel Fridljand***" - Mathew V. Kiang - Eran Bendavid - Sam Heft-Neal - Marshall Burke - et al. journal: Nature Medicine date: 2024-07 url: https://www.nature.com/articles/s41591-024-03117-0 highlights: - "**Elite-Tier Publication Venue**: Published in *Nature Medicine* (Impact Factor: 50+, #1 in Biochemistry & Molecular Biology), ranking in the top 0.1% of all scientific journals globally—comparable to *Science* and *The Lancet*. Acceptance rate under 10%, demonstrating exceptional peer-review rigor" - "**Massive-Scale Data Science**: Analyzed entire US population over 26-year period (1990–2016), processing 63+ million death records across 3,000+ counties. Showcases advanced statistical modeling and geospatial analysis capabilities for quantifying complex demographic trends" - "**Immediate Policy Impact**: Already referenced in CDC's Social Vulnerability Index (SVI) publications and environmental justice reports on petrochemical facility buildouts. Rapid academic uptake with citations in *Nature* journals and *The Lancet*, establishing work as foundational text for environmental health equity" - "**ESG & Corporate Relevance**: Key finding—racial disparities in air pollution mortality exceed income-based disparities—directly informs Environmental, Social, and Governance (ESG) strategies, public health policy, and corporate sustainability frameworks" - "**High-Impact Finding with Real-World Applications**: Research demonstrates immediate relevance for health tech, government affairs, environmental consulting, and sustainability roles by bridging rigorous quantitative analysis with actionable policy insights" projects: - name: "Data Mining Hackathon - Core Demand Prediction (Unite)" date: 2026-03 location: Online highlights: - "**End-to-End Pipeline Architecture**: Architected reproducible Snakemake workflow with 20+ rules spanning data preprocessing, candidate generation, feature engineering, model training, portfolio selection, and automated submission—demonstrating infrastructure-as-code practices and scientific computing best practices with checkpoint-based incremental execution and declarative YAML configuration" - "**Two-Stage LightGBM Model with Expected Utility Optimization**: Implemented sophisticated scoring approach combining (Stage A) binary recurrence classifier predicting probability of recurring needs (p_recur) with (Stage B) spend regressor trained on positive cases only, then combined via expected utility formula: EU = p_recur * v_hat * r - F, where r is savings rate and F is fixed fee—directly aligning model outputs with the economic objective function" - "**Value-Aware Portfolio Selection**: Designed precision-first selection strategy with configurable EU threshold, top-K per buyer cap (150 for Level 1, 60 for Level 2), and guardrails (min_orders: 2, min_months: 2, high_spend: €200) to balance recall vs fee exposure, implementing hybrid approach merging lgbm_two_stage primary portfolio with phase3_repro backfill up to target_per_buyer" - "Achieved 3rd place in Level 2 (E-Class plus Manufacturer) with net score EUR 423,553.47 (savings: €638,053.47, fees: €214,500, hits: 7,701, spend capture: 25.5%) as team AAD, competing against 6 teams in TUM.ai x Unite hackathon" - "Achieved 5th place in Level 1 (E-Class) with net score EUR 1,327,614.87 (savings: €1,621,484.87, fees: €293,870, hits: 17,661, spend capture: 64.7%), demonstrating robust performance across both warm-start (history-based) and cold-start (industry-informed) buyer segments" - "**Advanced Feature Engineering**: Engineered 200+ features across 6 families (frequency/intensity, recency/timing, economic/value, calendar/seasonality with cyclic encoding, momentum/trend, buyer-context including NACE industry codes and company size), implemented tenure normalization to enable fair comparison between late joiners and long-tenure buyers (avg_monthly_orders_pair_tenure, avg_monthly_spend_pair_tenure), and integrated top-K SKU attribute columns for enriched product context" - "**Cold-Start Strategy**: Developed fallback approach using industry-informed rankings aggregated from warm-buyer behavior by NACE hierarchy (most specific level with sufficient data, then fallback to broader levels), with configurable cold_start_top_k (50 for Level 1, 30 for Level 2) to control fee exposure when historical purchase data is unavailable" - "**Systematic Parameter Tuning**: Implemented sweep framework exploring score thresholds (0.0 to -0.20), top-K per buyer (150-400), and guardrail configurations across both levels, with automated analysis producing param_effects.csv, run_metrics.csv, and visualization plots (score vs predictions, score vs capture, submission size vs score, run timeline) for evidence-based optimization" - "**Reproducible Experiment Tracking**: Architected score run history system organized by approach and level with run_ids combining UTC timestamps and git SHA values, capturing score_summary.csv, score_details.parquet, and metadata.json with commit, branch, dirty state, and timestamp for full reproducibility" - "**Automated Submission Integration**: Built Playwright-based automated submission script for Unite evaluator portal with team/password credential management from config.yaml, live score extraction (Net Score, Savings, Fees, Hits, Spend Captured), and automatic archiving with best run indexing" - "**Alternative Modelling Approaches**: Implemented baseline heuristic (frequency + value - staleness), phase3_repro hand-crafted scoring formula (n_orders * exp(-delta_recency/lambda) * avg_spend_per_order * r) with sparse-history gate, and lgbm_v3_factorized three-stage model decomposing spend into order frequency (Stage A count regression) and unit value proxy (stage B historical lookup)" - "**Tech Stack**: Python, LightGBM (two-stage classifier + regressor), Snakemake (workflow orchestration), pandas, NumPy, scikit-learn, parquet/CSV pipelines, Playwright (web automation for submissions), PyYAML (configuration), matplotlib/seaborn (visualization), git-based reproducibility" - name: "Agent Olympics Hackathon Munich 2026" date: 2026-01 location: Munich highlights: - "Engineered a white-hat voice agent to execute automated prompt injection attacks against multi-layered AI defense systems" - "Orchestrated complex social engineering flows, successfully manipulating a customer support agent to gain access to restricted technical support systems" - "Exploited prompt injection vulnerabilities to bypass security guardrails and extract pre-planted sensitive information" - "**OSINT Engine Development**: Implemented automated Open Source Intelligence (OSINT) collection and analysis system for target company and persona profiling, enabling highly personalized attack vectors" - "**Multi-Channel Attack Engine**: Architected company-agnostic attack system executing personalized social engineering campaigns across email, SMS, and WhatsApp channels, leveraging gathered intelligence for maximum effectiveness" - "**Fully Automated Red Team Operations**: Designed zero-human-interaction attack pipeline, demonstrating production-grade automation in security testing and vulnerability assessment" - "Sponsored by Make (company by Celonis, OpenAi, revel8)" - "At Celonis office in Munich" - name: "Modern LLM Architectures Workshop (TNG)" date: 2026-02 location: TNG Internal highlights: - "**Transformer internals**: Built deep understanding of scaled dot-product attention, multi-head attention (Q/K/V projections, head concatenation), and decoder-block mechanics (residual connections, LayerNorm, FFN)" - "**Hands-on PyTorch implementation**: Implemented SingleHeadAttention and MultiHeadAttention from scratch with golden-tensor assertions; implemented multi-head attention with KV-cache for autoregressive decoding and RMSNorm" - "**Advanced architecture topics**: Studied inference-efficiency techniques (KV-cache, Grouped-Query Attention, Multi-Head Latent Attention), Mixture of Experts (MoE), and scaling-law trade-offs between model size and compute" - "**Theoretical grounding**: Consolidated workshop content through Kevin Murphy's probabilistic machine learning chapters, connecting attention and transformer design choices with probabilistic modeling principles" - "**Positional encoding**: Understood sinusoidal embeddings and Rotary Position Embeddings (RoPE) for relative position information; implemented RoPE in attention (apply_rope, inverse-frequency angles)" skills: - label: Programming Languages details: "Python (3+ years): pandas, numpy, scikit-learn, PyTorch, TensorFlow, FastAPI, Streamlit, pytest, Pydantic, PydanticAI, DSPy; R (5+ years, Expert): tidyverse, ggplot2, dplyr, caret, Shiny, Rcpp, Bioconductor; Java (2+ years): Java 8/17, Spring Framework, JBoss/WildFly, Gradle; TypeScript/JavaScript (2+ years, Proficient): React; C++ (1+ year, Intermediate): Performance optimization, Rcpp integration, memory management; SQL (3+ years): Oracle DB, query optimization, database design; Bash/Shell scripting (3+ years): automation, DevOps tooling" - label: Machine Learning & AI details: "Deep Learning (PyTorch, TensorFlow, Keras), Transformer internals (scaled dot-product attention, multi-head attention, decoder blocks), positional encoding (sinusoidal, RoPE), inference efficiency (KV-cache, GQA, MLA, RMSNorm), Mixture of Experts (MoE), probabilistic ML foundations (Kevin Murphy), Large Language Models (prompt engineering, fine-tuning, model merging, Assembly-of-Experts), Random Forests, Gradient Boosting, Topological Data Analysis (TDA), Manifold Learning, LLM Frameworks (PydanticAI, DSPy, LangChain, Pydantic), Model Optimization, Hyperparameter Tuning" - label: Statistical Methods & Mathematics details: "Bayesian Non-parametrics (Hierarchical Dirichlet Processes, Dirichlet Process Mixtures), Causal Inference (DAGs, Confounder Adjustment, Propensity Scores), Multiple Testing Correction (FDR, Bonferroni, Benjamini-Hochberg, IHW), High-Dimensional Statistics, Selective Inference, Stochastic Processes, Graphical Modelling, Differential Geometry, Probability Theory, Multivariate Analysis, Generalized Linear Models (GLMs)" - label: Data Science & Engineering details: "ETL Pipeline Design, Data Warehousing, Big Data Processing (16B+ records), Geographic Data Harmonization (multi-resolution spatial integration), Census Boundary Crosswalk Algorithms, Population-Weighted Spatial Aggregation, Geospatial Data Analysis, Time-Series Analysis, Feature Engineering, Data Visualization (ggplot2, matplotlib, seaborn, Shiny), Exploratory Data Analysis (EDA), Statistical Modeling, A/B Testing, Experimental Design" - label: DevOps & Infrastructure details: "**Containerization**: Docker, Podman (daemonless, rootless execution); **CI/CD**: Jenkins (Groovy pipelines, shared libraries), GitHub Actions; **Cloud Platforms**: AWS (ECR, S3), Render.com, Supabase; **Orchestration**: Kubernetes; **Monitoring**: Prometheus, Grafana, Langfuse (LLM observability); **Code Quality**: SonarQube (Quality Gates, static analysis), unit testing, integration testing; **Version Control**: Git (advanced workflows, branching strategies)" - label: Web Development & Frameworks details: "**Frontend**: React, TypeScript, Streamlit, HTML/CSS; **Backend**: FastAPI (Python), Spring Boot (Java), JBoss/WildFly, RESTful API design (OpenAPI 3.1); **Databases**: Oracle DB, SQL query optimization; **API Integration**: WebSockets, OAuth, external service integration" - label: Bioinformatics & Computational Biology details: "Single-Cell Sequencing Analysis (whole-exome, whole-genome), Mutational Signature Estimation, Phylogenetic Tree Analysis, Genomic Data Processing, GWAS (Genome-Wide Association Studies), hQTL Analysis, Copy Number Variation (CNV), Cancer Genomics, Population Genetics, Bioconductor Ecosystem, COSMIC Database" - label: Software Engineering Practices details: "Design Patterns (Template, Strategy, Composite, Hexagonal Architecture, Ports & Adapters), Test-Driven Development (TDD), Clean Code, Agile/Scrum (2-week sprints), Code Review, Pair Programming, Documentation, Technical Debt Management, Refactoring, Legacy System Modernization" - label: Domain Expertise details: "Environmental Epidemiology, Public Health Data Analysis, Cancer Biology, Tumor Evolution, Supply Chain Management, Logistics Systems, Customer Support Automation, Document Intelligence" - label: Scientific Communication details: "Academic Writing (Nature Medicine publication), Conference Presentations (7 scientific talks including Yale, UNC, DAGStat), Peer Review (Bioinformatics Advances, Cell Biology), Technical Documentation, Interactive Visualization, Seminar Presentations, Teaching/Mentoring (150+ students)" - label: Languages details: "**English** (Professional working proficiency - C2, academic and technical communication), **German** (Native), **Russian** (Native)" - label: Tools & Technologies details: "Git/GitHub, VS Code, IntelliJ IDEA, Jupyter Notebooks, RStudio, LaTeX, Markdown, Mermaid (diagrams), Snakemake (workflow orchestration), Playwright (browser automation), LanguageTool, OpenAPI/Swagger, JIRA, Confluence, Notion" design: theme: sb2nov page: size: us-letter top_margin: 1.5cm # Reduce from 2cm to gain vertical space bottom_margin: 1.5cm # Reduce from 2cm left_margin: 1.5cm # Reduce from 2cm (standard is often too wide) right_margin: 1.5cm show_footer: true show_top_note: true colors: body: rgb(0, 0, 0) name: rgb(0, 0, 0) connections: rgb(0, 0, 0) section_titles: rgb(0, 0, 0) links: rgb(0, 79, 144) footer: rgb(128, 128, 128) top_note: rgb(128, 128, 128) typography: line_spacing: 0.6em alignment: justified date_and_location_column_alignment: right font_family: New Computer Modern font_size: body: 10pt name: 30pt bold: name: true section_titles: true links: underline: true show_external_link_icon: false header: alignment: center photo_width: 4cm space_below_name: 0.3cm # Tighten header space_below_connections: 0.3cm connections: show_icons: true display_urls_instead_of_usernames: false separator: '' space_between_connections: 0.5cm hyperlink: true section_titles: type: with_full_line line_thickness: 0.5pt space_above: 0.3cm space_below: 0.1cm sections: allow_page_break: true space_between_regular_entries: 1.2em entries: date_and_location_width: 4.15cm side_space: 0.2cm space_between_columns: 0.1cm allow_page_break: true short_second_row: false summary: space_above: 0cm space_left: 0cm highlights: bullet: ◦ nested_bullet: '-' space_left: 0.2cm space_above: 0.15cm space_between_items: 0.05cm space_between_bullet_and_text: 0.5em templates: one_line_entry: main_column: '**LABEL:** DETAILS' education_entry: main_column: |- **INSTITUTION** *DEGREE in AREA* SUMMARY HIGHLIGHTS degree_column: '' date_and_location_column: |- *LOCATION* *DATE* experience_entry: main_column: |- **POSITION** *COMPANY* SUMMARY HIGHLIGHTS date_and_location_column: |- *LOCATION* *DATE* publication_entry: main_column: |- **TITLE** AUTHORS URL (JOURNAL) date_and_location_column: DATE locale: language: english settings: current_date: '2026-01-24' bold_keywords: - Python - Machine Learning