Company & Culture

EXANTE is a pioneering wealth tech company that delivers cutting-edge centralized trading solutions and robust B2B financial infrastructure, driving value through innovative technology. Our bespoke, internally engineered trading platform provides seamless access to a diverse range of financial instruments, including stocks, ETFs, bonds, futures, and options, all within a single, multi-currency account.

As a rapidly expanding global firm with over 700 talented employees from 70+ nationalities across 70 locations, we are a frontrunner in the financial sector. Our investment priorities are clear: We prioritize investing in our most valuable asset—our people. Join us in shaping the future of finance.

We are building our Intelligence Core — a new predictive analytics capability at the heart of our sales and client management operations. Instead of relying on gut feel and manual processes, we want every client decision to be informed by data: who is at risk of leaving, who is ready to grow, and where should we focus our attention.

This is a foundational hire. You won't be joining an established data science team and picking up tickets. You will be designing the models, defining the problems, and shaping how the business uses predictive intelligence. You will work closely with the Head of Sales AI Transformation, the Analytics & Sales Controlling team, and front-line sales managers who will act on what you build.

Responsibilities:

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

Requirments:

Must have:

• 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)

Mindset we value:

• You propose solutions before being asked. When faced with an ambiguous problem, your instinct is to define it and start building, not to wait for a specification.
• You start simple and earn complexity. Logistic regression before gradient boosting. A working baseline before a perfect model.
• You think about what happens after the model is trained. Who uses the output? How do they act on it? Is the score delivered early enough to matter?
• You are comfortable saying "I don't know yet, but here's how I'd find out.

What we offer

• • 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.

A group of disruptive technology experts created EXANTE. With an impressive track record in the industry and knowledge of the markets, our systems are built to democratize access to global financial instruments for professional traders and institutional investors.

Through our robust, cutting-edge trading platforms, you can offer clients access to comprehensive, global financial instruments and investment possibilities while keeping their data safe and private.

EXANTE is a global prime broker backed by proprietary technology and dedicated service. All regulated group companies and fully controlled affiliates and subsidiaries are committed to compliance with data protection laws, including the GDPR. We process personal data in line with the GDPR principles of fairness, purpose limitation, transparency, data minimisation, storage limitation and deletion, accuracy, confidentiality and integrity, and accountability. We rely on legitimate interests to assess and manage your application, after determining that our interests are not overridden by your rights and interests. You have the rights of access, rectification, data portability, erasure, restriction of processing, and objection to processing. To exercise your rights or contact our Data Protection Officer, please see our GDPR policy at https://exante.eu/gdpr/.

We retain recruitment data only for as long as necessary for the purposes described above, after which it will be securely deleted unless we are required to keep it longer to comply with law or you consent to a longer retention.

Where personal data is transferred outside the EEA, it will be protected to a standard at least equivalent to that sought by the GDPR. Personal data is safeguarded by technical and organisational measures that are reviewed and updated regularly. This website is not intended for individuals under 18 and we do not knowingly collect data relating to children .