Decision Analytics Consultant - Commercial Analytics + AI Engineering
Posted
Summary
A hybrid consultant role at ZS (Pune) splitting ~60% AI engineering — building LLM/agentic analytics pipelines on Databricks, NL-to-SQL, eval harnesses — with ~40% core commercial analytics (segmentation, causal inference, forecasting) for life-sciences clients, while leading a team of ~24 associates.
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 youll 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.
The role
;
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 24 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 Youll 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 24; 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 of CST talent.
; ;
What Youll Bring ;
- A bachelors or masters degree in engineering (any discipline) with strong academic performance, particularly in analytics and quantitative coursework
- 5 8 years of relevant job experience. An MBA/PhD may substitute in lieu of work experience
- SQL expert. Window functions, cohort and longitudinal logic, performance tuning on claims-scale data.
- Python production-grade. pandas/PySpark, packaging, testing, Git; you have owned code others depend on.
- Lakehouse platform Databricks (Delta, Unity Catalog, Spark, Workflows) or comparable (Snowflake/BigQuery + Spark).
- LLM application engineering Prompt and context engineering, RAG, function/tool calling, agent frameworks (Claude Agent SDK, LangGraph or equivalent), evaluation and monitoring. You can explain how your system failed and what you changed.
- Applied analytics solving business problems in a stakeholder-facing role, spanning more than one method family: diagnostic and cohort/longitudinal analysis, segmentation, test-and-control / causal inference, forecasting, predictive modelling; statistical rigour and explicit QC. Life-sciences or healthcare analytics experience strongly preferred any function (commercial, medical, market access, clinical development, operations); hands-on with real-world data (claims, EHR, registries), clinical/trial or engagement data a plus.
- Consulting skills. Structure ambiguity, own deliverables end to end, communicate crisply in PowerPoint, Excel and in the room.
- People leadership. Has led 2+ people on delivery with accountability for their output quality.
- Fluency in English
- Client-first mentality
- Intense work ethic
- Collaborative spirit and problem-solving approach
