Data Strategist

We are seeking a Data Strategist to join our dynamic Data & AI team. In this role, you will bridge business strategy, data, and emerging AI/ML capabilities to identify and shape high-value opportunities for organizational transformation. You should combine the problem-framing, stakeholder leadership, and strategic thinking of a business strategist with enough technical fluency in data systems, machine learning, and software architecture to evaluate how technology can support business objectives.

The Data Strategist will work across business, product, technology, data, and design teams to translate ambiguous business opportunities into actionable transformation strategies and technically credible roadmaps.

Travel for team and client meetings is required, typically up to 15%.

Responsibilities

• Work with multidisciplinary teams through complex projects, planning, conducting, and interpreting business strategy activities to inform the direction of the product. Methods will include any/all of the business strategy tools, adapted for the client and context.
• Identify opportunities where data, ML, and AI can create meaningful business value and use business objectives and customer needs to refine those opportunities and use cases with measurable outcomes.
• Evaluate the current-state data and technology landscape supporting proposed business initiatives, including how data is captured, stored, governed, and consumed across enterprise systems.
• Maintain expertise across ML and AI paradigms, from traditional models to generative and agentic systems to determine feasibility and fit for proposed AI/ML solutions.
• Develop data, AI, and technology transformation strategies that connect near-term initiatives to longer-term organizational capabilities.
• Create current-state and future-state capability maps, dependency maps, and transformation roadmaps. Define and create experiments, prototypes, and proof-of-concepts that can reduce uncertainty before significant investment.
• Communicate technical concepts and architectural tradeoffs to non-technical executives and business stakeholders and business objectives, constraints, and outcomes to technical teams.

Qualifications

• Experience in business strategy, digital transformation, product strategy, technology strategy, or a related discipline.
• Experience in evaluating AI applications, identifying risks, and uncovering opportunities for enhancement.
• Strong understanding of how modern data systems support business processes and analytical or AI capabilities.
• Working knowledge of machine learning concepts and the ML development lifecycle.
• Ability to prototype and create working proof-of-concept demonstrations with working code and systems integration.
• Ability to evaluate technical proposals and experience working directly with engineers, data scientists, architects, product teams, and business leaders.
• Strong analytical and problem-solving skills with the ability to structure ambiguous problems and turn them into actionable strategies.
• Excellent written, verbal, and presentation skills. An ability to communicate effectively across executive, business, and technical audiences.
• Ability to operate in a fast-moving environment and comfort with ambiguity and uncertainty.

Technical Fluency

The Data Strategist should be comfortable with learning and discussing client requirements around:

• Data storage paradigms like relational databases, data warehouses, and data lakes
• Data models and schemas at the conceptual, logical, and physical levels
• Data pipelines, batched versus streaming data, and ingestion architectures like ETL and ELT
• Cloud data platforms and platforms for business intelligence and analytics
• Machine learning workflows, including training, evaluation, and deployment
• Generative AI, AI agents, and tool-using systems
• Data security, privacy, and governance

Technical fluency in Python and JavaScript is required, though deep production implementation expertise is not.