Data Science & Data Engineering Lead / Manager (Straive)
Summary
Lead a team building and scaling data pipelines and ML models for an education technology company, ensuring data-driven insights and infrastructure support.
Data Science & Data Engineering Lead / Manager (Straive)
We are looking for Lead / Manager, Data Science & Data Engineering - Payments & Financial Services Consulting to join Straive
About Straive
Straive is a global leader in Data Analytics and AI solutions, helping organizations unlock the value of their data through advanced technology and deep domain expertise.
Backed by EQT, one of the world's leading private equity firms, Straive serves clients in 30+ countries with teams across eight countries, headquartered in Singapore.
Join Straive to work on impactful, enterprise-scale AI and data projects in a fast-growing, innovation-driven environment.
The Engagement
This role leads delivery on a long-running consulting program for a leading global payments network (a major financial institution operating at the centre of the card-payments ecosystem). The work spans data science and data engineering initiatives delivered for the client and its broader ecosystem of partners - including acquiring banks and merchants.
Typical problems include building analytics and machine-learning solutions over large-scale transaction data, designing data pipelines and feature platforms, and translating commercial questions into measurable, production-grade data products.
Note: Specific client details are confidential and will be shared with shortlisted candidates under NDA.
About the Role
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 Responsibilities
• Delivery 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 Qualifications
• 6-8 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 Qualifications
• AWS (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.
This job description is not intended to cover or contain a comprehensive listing of all responsibilities, duties, or activities that are required. Responsibilities, duties, and/or activities may change, or new ones may be added at any time with or without notice.
If you are a motivated professional with a passion for delivering impactful solutions, we’d love to hear from you. Apply today to be part of a dynamic and forward-thinking team at Straive.