Decision Analytics Consultant - Commercial Analytics + AI Engineering

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ZS is a place where passion changes lives. As a management consulting and technology firm focused on improving life and how we live it, we transform ideas into impact by bringing together data, science, technology and human ingenuity to deliver better outcomes for all. Here you’ll work side-by-side with a powerful collective of thinkers and experts shaping life-changing solutions for patients, caregivers and consumers, worldwide. ZSers drive impact by bringing a client-first mentality to each and every engagement. We partner collaboratively with our clients to develop custom solutions and technology products that create value and deliver company results across critical areas of their business. Bring your curiosity for learning, bold ideas, courage and passion to drive life-changing impact to ZS.

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Insights & Analytics

ZS’s Insights & Analytics group partners with clients to design and deliver solutions to help them tackle a broad range of business challenges. Our teams work on multiple projects simultaneously, leveraging advanced data analytics and problem-solving techniques. Our recommendations and solutions are based on rigorous research and analysis underpinned by deep expertise and thought leadership.

The role

A build-and-advise role. You lead build pods on AI-enabled analytics products and independently own core commercial analytics problems for the same client teams. Expect roughly a 60/40 split between AI engineering and core analytics, flexing with the release cycle. You own workstream-level delivery, manage 2–4 Associates / Associate Consultants, and are the day-to-day technical counterpart for client analytics and enterprise-AI stakeholders.

This is not a pure AI/ML engineering role. When build demand is low, you are staffed on core analytics workstreams for the same client; when it is high, you lead the build. Both halves are required.

What You’ll Do

• AI & analytics engineering (~60%)

  Own end-to-end build of insight pipelines on Databricks: certified Delta tables metric layer signal detection LLM narrative - application surface. - Design agentic workflows on an agent SDK (Claude Agent SDK or equivalent): tool design, context and prompt engineering, governed context stores, fallbacks and failure handling. - Enforce guardrails in data and metadata — governed tables, SQL-level scope control — rather than in prompt instructions. - Build evaluation harnesses for LLM output: answer-quality rubrics, binary gates (numerical fidelity, grounding, compliance), regression suites, release gates; tracing via Langfuse/LangSmith or equivalent. - Deliver conversational analytics (NL-to-SQL) on governed semantic layers (Databricks Genie, Metric Views) with certified KPI definitions. - Set engineering standards: code review, Git/CI, unit and SIT test plans, UAT support, release notes, observability, cost and latency tracking. - Produce architecture documents, security / IT-risk evidence packs and status reporting with honest status legends (implemented / in-flight / planned / blocked).

  Core commercial analytics (~40%)

  • Take ambiguous questions from any client function — commercial, medical, market access, clinical development, supply chain, enterprise operations — and structure them into decision-oriented analytical plans; own them end to end.
  • Pick the method the question needs — diagnostic analysis, cohort and longitudinal analysis, segmentation, forecasting, test-and-control and causal inference, predictive modelling, optimisation — and defend the choice.
  • Work across the client’s data estate — syndicated and patient-level real-world data, clinical and trial data, engagement and CRM data, payer and access data, operational and financial data — integrating sources and resolving definitional conflicts.
  • Establish governed definitions (KPIs, cohorts, business rules) with the client and make them enforceable in code — one definition, every surface.
  • Convert results into decisions: QC’d, client-ready narratives and decks; activation into workflows, dashboards or products; present to Director+ stakeholders.

  Client & team leadership

  • Plan, review and coach a team of 2–4; QC everything before it reaches the client.
  • Own workstream status, risks and dependencies with ZS leadership and client PMO.
  • Contribute to proposals, POVs and CST capability building — reusable assets and AI upskilling o…