Data Scientist

🕒 April 30

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EXANTE

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 2011

💳 Fintech

💸 Finance

🤝 B2B

Fintech • Finance • B2B

EXANTE is a global prime broker and fintech firm founded in 2011 that provides direct access to over 2 million financial instruments across 50+ markets through a proprietary, customizable trading platform (desktop, web, mobile) with API and white-label options. It serves institutional and professional clients — banks, brokerages, asset managers, family offices, and professional traders — offering trade execution, custody arrangements, regulatory coverage, and dedicated relationship and post-trade support.

📋 Description

• 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

🎯 Requirements

• 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)

🏖️ Benefits

• Health insurance • 401(k) matching • Flexible work hours • Paid time off • Professional development opportunities