Beschreibung

I am a mathematician with several years of experience in teaching and research, now focusing on applying quantitative methods to real-world problems in risk and decision-making.

I help businesses understand uncertainty using:

statistical analysis
simulation models (Monte Carlo)
data-driven insights

What I can help you with

Risk modeling and scenario analysis
Monte Carlo simulations (Python / Excel)
Statistical analysis and interpretation
Forecasting and decision support tools
Clear explanation of complex results

How I work

I focus on:

simple and transparent models
clear communication of results
practical solutions rather than theoretical complexity

Background

Strong foundation in mathematics and probability, with experience translating complex concepts into usable tools and insights.

Sprachen

Englisch
Muttersprachlich oder zweisprachig
Deutsch
Verhandlungssicher

Arbeitsortpräferenzen

Vor Ort möglich

Zurich (bis zu 50 km)

University of Zurich
Research Assistant – Data Science & Optimization
Oktober 2022 - März 2023 (5 Monate)
Collected and structured large-scale quantitative datasets (50,000–200,000+ records) across multiple analytical workflows, enabling data-driven decision-making for optimisation research and ensuring high data integrity.
Validated data quality and resolved inconsistencies across datasets, applying rigorous cross-checks to maintain analytical reliability.
Built reproducible analytical pipelines in R, reducing data preparation and reporting turnaround time by approximately 30%, freeing capacity for higher-value analysis.
Performed exploratory data analysis and statistical diagnostics to detect anomalies, inconsistencies, and variability patterns.
Statistical Analysis & Inference Data collection and analysis Quantitative data analysis R/Python Data visualization
Basel School of Business / Cesar Ritz Colleges Lucerne
Lecturer – Quantitative & Statistical Methods
Januar 2018 - Juli 2022 (4 Jahre und 6 Monate)
Delivered applied statistics and quantitative methods courses to 400+ students across multiple programmes over 4+ years, achieving a teaching evaluation score of 4.6/5 through clear communication and structured problem-solving.
Designed case-based exercises using real-world datasets to teach probability, uncertainty, and decision making under risk.
Restructured course materials to emphasise practical data interpretation, contributing to measurably improved student engagement and comprehension across all cohorts.
statistical analysis Teaching Complex Concepts Clearly probability Presentation of technical concepts Microsoft Excel
Independent
Claims & Risk Analysis
März 2026 - April 2026 (1 Monat)
•Analysed multi-variable insurance claims datasets to identify claim frequency, severity patterns, and loss variability across policy lines, benchmarking findings against industry reserving assumptions.
•Identified tail-risk exposure, anomaly patterns, and claims development trends using EDA and statistical diagnostics to inform non-life reserving and pricing decisions.
•Designed structured data validation workflows in Python and R to improve dataset reliability across large insurance datasets (50,000–200,000+ records).
•Evaluated claims development behaviour and reserve uncertainty with reference to IFRS 17 and Solvency II reserving principles.