We are seeking a Data Scientist to design, build and validate the machine learning models behind personalization features in our new mobile trading app, working within guardrails set by our Compliance team. What they will do Build similar-symbol models (clustering / co-occurrence) on aggregate trading data, including minimum-activity thresholds Develop propensity models that score customers’ likelihood to adopt new products, from day one of the customer lifecycle Explore models linking significant price moves to news events Define features, training data and evaluation metrics; validate models and set score thresholds Analyze content and in-app engagement data to identify drop-off and high-performing content Hand off production-ready models to ML Engineers and document methodology and assumptions
Требования: Must have 4+ years in applied data science, with models deployed to production Strong Python and SQL; scikit-learn, XGBoost/LightGBM or similar Recommendation systems, clustering and propensity / classification modeling Experimental design and model evaluation (A/B testing, calibration, drift) Ability to explain modeling choices clearly to product and compliance stakeholders Nice to have Brokerage, trading or other financial services experience Knowledge of options and equities markets Product analytics data (e.g. Amplitude)