I’m looking for someone who can move comfortably between rigorous statistical analysis and hands-on finance work. Day to day, you will take raw data from our investment research team, structure it into clean datasets, and then build investment analysis models that clarify where capital should flow next. Key pieces of the role – Apply one-way and two-way ANOVA tests (plus post-hoc checks where needed) to uncover statistically significant performance drivers in our portfolios. – Translate those findings into risk metrics and scenario outputs we can drop straight into our decision decks. – Develop and maintain investment-focused financial models that project cash flows, IRRs, and NPV under multiple market assumptions. – Produce concise, well-visualized interpretation notes so non-quant colleagues understand the story behind the numbers. I’m already clear on the three areas that matter most: financial modeling, risk analysis, and data interpretation. The modeling work is anchored in investment analysis—so experience with assets, returns, and sensitivity testing is vital. You’re free to use Excel, Python (pandas, statsmodels), R, or similar tools, provided results are reproducible and auditable. Deliverables • Clean, commented model files with input sheets separated from calculations • ANOVA output tables and visual summaries (plots, heatmaps, or dashboards) • A short memo (2–3 pages) explaining conclusions, limitations, and recommended next steps If this sounds like your wheelhouse, let’s talk and set up a workflow that gets rapid insights into the hands of our investment committee.