Insight Global
Data Science Product Manager
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Insight Global is seeking a Data Science Product Manager for a leading global financial services and analytics organisation.
This position requires remote working in the UK for a 12-month contract-to-hire opportunity (Inside IR35).
The successful candidate will play a key role in defining product vision, managing priorities, and delivering enterprise-scale data science, analytics, and AI products. You will work closely with Data Science, Engineering, Architecture, Governance, and business stakeholders to translate complex business challenges into scalable data-driven solutions that support analytics, risk, and AI initiatives.
You will be responsible for managing roadmaps and product backlogs, defining measurable outcomes, driving stakeholder alignment, ensuring compliance and governance requirements are met, and maximising business value through the delivery of innovative data products.
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Must-Have Requirements Include:
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- Experience in Product Ownership, Product Management, Data Science, Analytics, AI/ML, Data Platforms, or related technology disciplines
- Experience defining product vision, roadmaps, priorities, backlogs, and success metrics for data, analytics, or AI products
- Strong experience with data quality, master data management (MDM), metadata management, knowledge graphs, entity resolution, or data confidence/scoring solutions
- Proven track record delivering data science, machine learning, AI, analytics, or enterprise data products from discovery through production adoption
- Strong understanding of product strategy, roadmap planning, prioritisation, Agile methodologies, backlog management, and outcome-driven delivery
- Experience translating business requirements into user stories, acceptance criteria, delivery milestones, and measurable business outcomes
- Working knowledge of data modelling, ETL/ELT processes, SQL, APIs, cloud data platforms, MLOps, and analytics workflows
- Familiarity with Generative AI, AI governance, model risk management, explainability, responsible AI, and regulated AI deployment practices
- Hands-on exposure to Databricks, SQL, Python, Git, cloud platforms, CI/CD pipelines, performance optimisation, and modern data architectures
- Strong understanding of data governance, privacy, security, regulatory compliance, AI risk, and responsible AI principles
- Excellent stakeholder management, communication, facilitation, and problem-solving skills
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