Define and build predictive models from scratch, starting with:
Work with raw trading, transactional, and behavioral data from our data warehouse
Define target variables and operationalize business concepts (e.g., what constitutes "churn" in a brokerage context) into measurable ML targets
Engineer features from client activity, trading patterns, market conditions, and engagement signals
Select, train, validate, and iterate on models — starting simple, increasing complexity where it earns its keep
Design monitoring for model performance, data drift, and degradation over time
Deliver daily client-level scores that integrate into CRM workflows and sales processes
Translate model outputs into actionable insights for non-technical sales managers
Work with sales leadership to design interventions around model predictions
Present results, assumptions, limitations, and recommendations to senior stakeholders

4+ years of hands-on experience building and deploying predictive models on real business problems (classification, regression, scoring)
Strong proficiency in Python (pandas, scikit-learn, XGBoost/LightGBM/CatBoost) and SQL
Demonstrated ability to independently frame ambiguous business problems as ML tasks — define the target, engineer the features, choose the approach
Experience with tabular data at scale: feature engineering, handling class imbalance, temporal validation, avoiding data leakage
Ability to communicate model results to non-technical stakeholders in plain, actionable language
Experience working with time-series or event-based behavioral data.
Experience with churn prediction, propensity modeling, CLV, or customer scoring in any industry (strong advantage)
Familiarity with survival analysis (Cox proportional hazards, time-to-event modeling) (strong advantage)
Experience with model monitoring in production: data drift detection, retraining pipelines, champion-challenger frameworks (strong advantage)
Background in financial services, brokerage, or fintech (strong advantage)
Experience with probabilistic models for CLV (BG/NBD, Pareto/NBD, Gamma-Gamma) (strong advantage)
Familiarity with SHAP, LIME, or other model interpretability techniques (strong advantage)
Experience with data warehousing tools (BigQuery, Databricks, or similar) (strong advantage)

Data Scientist

Key skills