Senior Data Scientist
- Responsible for the measurement and experimentation strategy for Genius.
Resume
Staff Data Scientist specialised in causal inference and experimentation. I turn ambiguous questions into analytical frameworks that move decisions. 8+ years of data science across marketplaces and B2B SaaS in European and global markets.
Experience
Staff Data Scientist — experimentation and causal inference
- Owned the experimentation maturity strategy. Led a structured POC with a cross-functional panel to onboard Eppo as the company-wide platform; democratised experimentation across the product organisation (article).
- Informed leadership's global rollout decision on a new pricing model. Built a synthetic experiment pipeline (Abadie & Zhao, 2026) across geo-level holdouts.
- Established an evidence-based product discovery process with a cross-functional squad, scoring rubric, and hypothesis tracker. Redirected the H2 roadmap from feature-building to solving the real adoption barrier.
- Gave the company its first method to estimate long-term experiment impact from short-term data (Athey et al. 2024).
Staff Data Scientist — experimentation and causal inference
- Increased transactional services adoption by 8% at stable revenue. Identified differential pricing as the highest-leverage growth lever and designed a multi-cell experimentation programme.
- Led global leadership to invest in listing detail page improvements by pinpointing which item page properties drive higher conversion across German and Dutch marketplaces.
- Uncovered inefficiencies costing an estimated 14% of transactions through cross-tribal funnel diagnostics spanning 2 product tribes. Mentored senior data scientists in securing stakeholder buy-in.
- Founded a recurring internal experimentation conference; cross-market forum where champions shared methods, challenged designs, and raised testing rigour.
Senior Data Scientist — experimentation and causal inference
- Built the standard experimentation toolkit across eBay Classifieds markets. Co-developed the internal Python package as core developer and statistician; cut setup by 3h per experiment, runtime by 20%.
- Trained 150+ colleagues in structured opportunity finding through an internal Analytics University (trainer score 9+/10). Graduates independently designed experiments that previously required analyst support.
Data Scientist, Global — product
- Informed investment decisions on which product categories to specialise by identifying verticalisation opportunities through network modelling and simulation.
- Shaped global fraud prevention strategy by quantifying fraud exposure's causal impact on marketplace trust and transaction volume using econometric methods.
Intern Data Scientist
- Validated customer experience metrics (Customer Effort Score, Smile Metric) through quantitative analysis and behavioural deep-dives.
Skills
Organisational
Storytelling with data Verbal and written communication Stakeholder management
Modelling, Experimentation & Causal Inference
DoubleML Diff-in-Diff IPSW Regression Discontinuity Design Interrupted time series Synthetic Control Regression Instrumental Variables A/B Testing Causal forests Econometric Methods
Machine Learning & AI
Gradient boosting Random forests Clustering Dimensionality reduction
Programming
Python R SQL Object-Oriented Programming Git Spark Google Cloud Platform AWS Snowflake Databricks
Languages
English (proficient) Dutch (native) Spanish (native) French (advanced)
Education
MSc, Behavioural Data Science
BSc, Psychological Research Methods and Statistics
Projects and Activities
Causal Inference Blog
Tech-blog on causal inference and online experimentation
Conference Moderator
Moderator at CIRCUS experimentation conference in London — by Datadog
Research Publication
Paper co-author of experimentation in industry (WIP)