Anticipate Trends, Reduce Risk, and Drive Proactive Decisions
Expert predictive analytics consultant in Switzerland specializing in statistical modeling, time-series forecasting, and demand planning. I build production-grade predictive models using Python, R, and modern ML frameworks that help businesses forecast revenue, reduce churn, optimize pricing, and make data-driven decisions with confidence.
Proven statistical modeling expertise across regression, classification, and time-series methods
Fluent in Python (scikit-learn, statsmodels, Prophet) and R for end-to-end predictive workflows
Time-series forecasting achieving 15–30% improvement over baseline heuristic methods
Demand planning models that reduce inventory costs while maintaining 95%+ service levels
Customer lifetime value (CLV) scoring to prioritize high-value acquisition and retention strategies
Robust model validation with cross-validation, backtesting, and confidence interval estimation
Automated model retraining pipelines ensuring predictions stay accurate as data evolves
Clear, interpretable outputs — stakeholders understand the why behind every prediction
Swiss-based with deep experience in financial services, retail, pharma, and manufacturing sectors
Build accurate revenue prediction models using historical patterns, seasonality, external factors, and leading indicators — enabling proactive budgeting, hiring, and resource allocation.
Identify customers at risk of leaving before they churn using behavioral signals, engagement patterns, and predictive scoring — with actionable retention recommendations for each segment.
Forecast product demand at granular levels (SKU, region, channel) to optimize inventory, reduce stockouts, minimize overstock, and improve supply chain efficiency by 20–35%.
Develop predictive risk scores for credit decisioning, fraud detection, and insurance underwriting — compliant with Swiss and EU regulatory frameworks including FINMA and GDPR.
Model price elasticity and competitor dynamics to find optimal pricing strategies that maximize revenue or margin — tested with A/B experiments and causal inference methods.
Deploy automated anomaly detection systems that flag unusual patterns in financial transactions, operational metrics, or sensor data — reducing response time from days to minutes.