Lead / Manager, Data Science & Data Engineering

As Lead, you will own end-to-end delivery for a portfolio of data science and data engineering projects, managing a team of approximately five practitioners. You will be the senior technical and delivery point of contact for client stakeholders - setting solution direction, ensuring quality and timeliness, and growing the capability of your team. This is a hands-on leadership role: you are expected to remain close to the data and the code while steering strategy and people.Key ResponsibilitiesDelivery leadership: Own scoping, planning, and execution of data science and data engineering workstreams; ensure on-time, high-quality delivery against client commitments.Team management: Lead, mentor, and develop a team of ~5 data scientists and data engineers; allocate work, review output, and support career growth.Stakeholder partnership: Serve as the primary technical liaison to client and partner stakeholders (including acquirer- and merchant-facing teams); translate business goals into analytical solutions and communicate results to technical and non-technical audiences.Solution design: Architect end-to-end solutions - from data ingestion and pipelines to modelling, evaluation, and deployment - over large-scale, sensitive financial datasets.Hands-on contribution: Write and review production-grade Python and SQL; set engineering standards for reproducibility, testing, and code quality.Modelling & analytics: Guide the development of statistical and machine-learning models (e.g., segmentation, propensity, forecasting, anomaly/risk signals) tuned to payments and financial-services use cases.Governance & quality: Ensure compliance with data privacy, security, and governance requirements appropriate to regulated financial data.Practice growth: Contribute to estimation, staffing, and proposals; identify opportunities to expand the engagement's scope and impact. Required Qualifications6-8 years of experience delivering data science projects for financial institutions (banking, payments, cards, lending, or similar regulated environments).Python - mandatory: Expert, production-level proficiency for data science and engineering (e.g., pandas, scikit-learn, and standard ML/data tooling).SQL - mandatory: Advanced proficiency working with large relational/analytical datasets, including performance-aware query design.Demonstrated ownership of end-to-end delivery - from problem framing through deployment - with measurable business outcomes.Experience leading or mentoring a team and managing delivery against client or stakeholder commitments.Strong communication skills; able to engage senior stakeholders and explain technical concepts clearly.Bachelor's or Master's degree in Computer Science, Statistics, Engineering, Mathematics, or a related quantitative field (or equivalent practical experience).Authorization to work in the United States and ability to work onsite in the San Francisco Bay Area. Preferred QualificationsAWS (cloud) - preferred: Hands-on experience building and deploying data/ML workloads on AWS (e.g., S3, Glue, EMR, Redshift, SageMaker, or equivalent services).Direct experience in the payments ecosystem (issuers, acquirers, networks, or merchants) and familiarity with transaction-level data.Experience with modern data engineering practices: orchestration, data modelling, CI/CD, and large-scale distributed processing (e.g., Spark).Exposure to MLOps, feature stores, and model monitoring in production.Prior consulting or client-facing delivery experience.