Vice President, Data Science

Overview

As Data Scientist Leader you steer the design, development and operation of high-rigor analytical and ML systems for a regulated financial services estate. You shape the AI/ML roadmap for Enterprise Solutions and deliver production-grade models for anomaly detection, forecasting and behavior signals. You collaborate with engineering and product teams to take models from problem framing to deployment, monitoring and continuous improvement, ensuring explainability and regulatory readiness. This is a hands-on, strategy-led role with a strong impact on risk, compliance and performance.

Pay / Benefits

• Health & Wellness
• Flexible Downtime
• Continuous Learning
• Invest in Your Future
• Family Friendly Perks
• Beyond the Basics

Responsibilities

• Lead the full model lifecycle: exploration, feature engineering, development, back-testing, deployment, monitoring and tuning.
• Deliver production-grade ML/analytic models in a regulated, data-intensive environment (finance)
• Ensure models are explainable, robust, and compliant under operational and regulatory scrutiny
• Manage deployment patterns, model serving, performance trade-offs, and failure modes in production
• Handle large, complex, and imperfect datasets with evolving schemas and noisy labels
• Monitor models over time, implement drift detection, retraining strategies, and incident response
• Provide technical mentorship and influence through reviews, pairing and leadership without heavy People management
• Collaborate with data platform, engineering and product teams to translate problems into end-to-end solutions

Key requirements

• 25+ years of analytics, data science, or ML in production
• extensive experience in financial services or regulated high-availability domains
• deep grounding in statistics, ML, time-series analysis and predictive modelling
• hands-on ownership of data exploration, feature engineering, modelling, back-testing, deployment and monitoring
• ability to work with large, complex datasets and imperfect data scenarios
• understanding of production ML system design, including batch vs real-time inference and model serving
• experience with drift detection, versioning, retraining strategies and incident response
• experience designing explainable models with feature attribution and transparency
• ability to combine statistical, ML, semantic models and rules-based logic for accuracy and explainability
• strong communication skills with engineers, product partners and senior stakeholders
• mentorship capacity with review and pairing of other data scientists

• clear communicator
• pragmatic and outcome-driven
• collaborative and cross-functional
• statistics
• time-series analysis
• machine learning (production-grade)

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