Data Scientist

🕒 June 2

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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: • Churn prediction — identify clients at risk of reducing activity or withdrawing assets, early enough for the sales team to act • Upsell / cross-sell propensity — score clients by their likelihood to increase assets under custody, and surface the right opportunities • Client Lifetime Value (CLV) — estimate forward-looking client value to guide resource allocation and relationship management prioritization • Own the full modeling lifecycle: • 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 • Bridge data science and business: • 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. • Strong advantage: • Experience with churn prediction, propensity modeling, CLV, or customer scoring in any industry • Familiarity with survival analysis (Cox proportional hazards, time-to-event modeling) • Experience with model monitoring in production: data drift detection, retraining pipelines, champion-challenger frameworks • Background in financial services, brokerage, or fintech • Experience with probabilistic models for CLV (BG/NBD, Pareto/NBD, Gamma-Gamma) • Familiarity with SHAP, LIME, or other model interpretability techniques • Experience with data warehousing tools (BigQuery, Databricks, or similar)

🏖️ Benefits

• Competitive salary that reflects your experience and the value you bring. • Flexibility that fits your life — work from home, from our office, or a mix of both. You decide what works best. • Flexible benefits package — choose the options that suit your life, not a one-size-fits-all bundle. • A genuinely good place to work — an informal, collaborative culture where ideas are heard and bureaucracy stays out of your way. • Continuous learning — ongoing training, education programs, and the support to deepen your expertise in a fast-moving industry. • Connection beyond your desk — events that bring our teams together to network and celebrate. • Global exposure — work side by side with talented colleagues from all over the world, across a business serving clients in 100+ countries.