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
Production pipeline supporting 10+ fraud models across billions of dollars in e-commerce transactions, with MLOps observability and multi-cloud batch inference patterns.
Built around model serving, validation, evaluation, and monitoring workflows using Python, SQL, cloud infrastructure, and MLOps patterns.
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.
AI agents
AI agent that generates Looker dashboards from BigQuery schema context, turning analytics intent into validated self-serve fraud reporting.
Commerce automation
Built a Shopify app from scratch for EU-compliant invoice generation, integrating with Billit and automating billing workflows.
Startup
Co-founded an eyewear company scaled to EUR300k ARR, with product strategy, DTC growth, operations automation, and EUR150k in angel funding.
Visit siteOpen source
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, scikit-learn, AWS, GCP, CI/CD, Looker, APIs.
LLMs, LLM evals, multi-agent systems, tool integrations, retrieval, orchestration, enterprise automation.
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.