Minimum of 4+ years of hands-on experience in data science within high-performing product analytics environments, ideally supporting B2B software platforms.
Advanced proficiency in Python and SQL, including strong working knowledge of pandas, NumPy, StatsModels or Scikit-learn, and experience working within modern data warehouses such as BigQuery or Snowflake.
Strong command of statistical sampling methodologies, including stratified sampling, bootstrapping techniques, and reliable extrapolation approaches.
Proven track record of independently designing, building, and deploying analytics tools or frameworks that were successfully adopted by product, growth, or cross-functional teams.
Deep expertise in experimentation and causal measurement, including A/B testing, difference-in-differences analysis, propensity score matching, matched market testing, and statistical power calculations.
Hands-on experience working with large language models (LLMs), including prompt design, embedding strategies, and evaluating or mitigating hallucination risks.
Exceptional written and verbal communication skills, with experience delivering executive-ready insight reports tailored to product, design, and engineering stakeholders.
Highly autonomous operator who performs well in ambiguous environments, prioritizes effectively, and drives initiatives from concept through completion with minimal oversight.
Demonstrated ability to quickly adopt new tools and methodologies, consistently implementing process improvements that reduce analysis turnaround time.