Senior Data Scientist / AI Product Builder / Co-Founder
Building and productionizing ML and AI systems for real-world problems, from fraud detection, anomaly detection, agentic analytics, to enterprise automation. My work is grounded in thorough evaluation, user empathy, and systems that improve real decisions.
Senior Data Scientist on Walmart's global fraud prevention team, focused on production ML systems, agentic workflows, evaluation, and measurable product trade-offs.
My background includes a PhD in Business Economics, with research on machine learning for time-series and sensor data. I also co-founded Naos Optics, an eyewear company and scaled it to EUR300k ARR. The common thread is turning real-world problems into practical systems people actually use.
MLOps
Co-built an end-to-end platform supporting 12 fraud models, each trained on roughly 100M rows, across billions of dollars in e-commerce transactions.
Reusable Python packages and MLflow workflows reduced model-development cycles from months to days.
AI enablement
Co-built Model Sherpa, a specialized AI skill that guides data scientists through a production ML platform, turning its workflows and engineering standards into just-in-time, context-aware support.
Helps practitioners navigate training, evaluation, model registration, and promotion without needing to learn the platform by trial and error.
Fraud
Fraud detection systems for changing abuse patterns, balancing loss prevention, false positives, operational cost, and customer experience.
Anomaly detection
Agentic anomaly detection for monitoring distribution shifts, combining time-series methods, LLM workflows, evaluation, and automated triage.
Applied AI
AI systems that connect models to the tools where analytics, planning, investigation, and communication happen.
Generates validated dashboards from BigQuery schemas and analytics intent.
Structures goals and working notes into tasks, owners, timelines, and updates.
Investigates logs and dashboards to surface anomalies and summarize evidence.
Turns briefs and source material into interactive presentation web apps rather than static slide decks.
Commerce automation
Built a Shopify app from scratch for EU-compliant invoice generation, integrating with Billit and automating billing workflows.
Open source / MCP
Built a typed Python MCP server for invoice retrieval, exact payment-reference lookup, invoice creation, and payment updates in Billit.
Designed for safe agent use with environment-only credentials, idempotent writes, production safeguards, normalized outputs, and a fully tested API layer.
View on GitHubStartup
Co-founded an eyewear company scaled to EUR300k ARR, with product strategy, DTC growth, operations automation, and EUR150k in angel funding.
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Contributor to Code Puppy, Walmart's open-source AI coding tool for accelerating developer workflows.
Read articleFraud prevention work across production ML, agentic anomaly detection, and analytics automation.
Product, brand, operations, automation, and international growth for Naos Optics, an eyewear company.
Causal ML and Bayesian modeling for European marketing and video advertising insights.
Machine learning for time-series, sensor data, smart agriculture, and self-supervised anomaly detection.
Client-facing analytics, computer vision, text mining, product roadmaps, and innovation workshops.
My research focused on machine learning for time-series and sensor data, with applications in anomaly detection, forecasting, self-supervised learning, and smart agriculture.
Google Scholar
At Naos Optics, my work spans product strategy, innovation, operations, automation, distribution, and growth. The company reached EUR300k ARR and raised EUR150k in angel funding while running a lean international DTC operation.
That work shaped how I think about building: useful products need user empathy, clear positioning, operational discipline, and systems that make the business easier to run.
Forecasting, anomaly detection, fraud detection, causal inference, foundation models, evaluation.
Python, SQL, JavaScript, PyTorch, AWS, GCP, BigQuery, CI/CD, Looker, APIs.
Agent loops, skills, plugins, MCP, tool use, multi-agent systems, LLM evaluations, and orchestration.
Product development, user research, stakeholder alignment, entrepreneurship, go-to-market, international growth.
Open to conversations about AI product work, data science & ML roles, applied AI systems, startups, and technical collaboration.