Z

ZS

Global management consulting for sales optimization

Decision Analytics Consultant - Commercial Analytics, AI Engineering

AI Application EngineeringConfirmed live in the last 24 hours
Full-timePune, Maharashtra, IndiaNo salary listed

About the Job

Requirements

  • A bachelor's or master's degree in engineering in any discipline, with strong academic performance, particularly in analytics and quantitative coursework.
  • Five to eight years of relevant job experience; an MBA or PhD may substitute for work experience.
  • Expert SQL skills, including window functions, cohort and longitudinal logic, and performance tuning on claims-scale data.
  • Production-grade Python skills, including pandas or PySpark, packaging, testing, and Git, with experience owning code others depend on.
  • Experience with a lakehouse platform such as Databricks (Delta, Unity Catalog, Spark, Workflows) or a comparable platform such as Snowflake or BigQuery with Spark.
  • LLM application engineering experience with prompt and context engineering, retrieval-augmented generation, function/tool calling, agent frameworks such as Claude Agent SDK or LangGraph, evaluation, and monitoring.
  • Applied analytics experience solving business problems in a stakeholder-facing role across more than one method family, including diagnostic and cohort/longitudinal analysis, segmentation, test-and-control or causal inference, forecasting, and predictive modelling; statistical rigour and explicit quality control are required.
  • Consulting skills to structure ambiguous problems, own deliverables end to end, and communicate in PowerPoint, Excel, and in person.
  • Experience leading at least two people on delivery with accountability for their output quality.
  • Fluency in English.

Responsibilities

  • Own end-to-end development of insight pipelines on Databricks, from certified Delta tables through the metric layer, signal detection, LLM narrative, and application surface.
  • Design agentic workflows using an agent SDK such as Claude Agent SDK or an equivalent, including tool design, context and prompt engineering, governed context stores, fallbacks, and failure handling.
  • Enforce data and metadata guardrails through governed tables and SQL-level scope control rather than prompt instructions.
  • Build evaluation harnesses for LLM output, including answer-quality rubrics, binary gates for numerical fidelity, grounding, and compliance, regression suites, release gates, and tracing through Langfuse, LangSmith, or an equivalent.
  • Deliver conversational analytics (natural-language-to-SQL) on governed semantic layers such as Databricks Genie and Metric Views, using certified KPI definitions.
  • Set engineering standards for code review, Git/continuous integration, unit and system integration testing plans, user acceptance testing support, release notes, observability, and cost and latency tracking.
  • Produce architecture documents, security and IT-risk evidence packs, and status reporting using clear implemented, in-flight, planned, and blocked status labels.
  • Structure ambiguous questions from client functions—including commercial, medical, market access, clinical development, supply chain, and enterprise operations—into decision-oriented analytical plans and own them end to end.
  • Select and defend analytical methods suited to the question, including diagnostic analysis, cohort and longitudinal analysis, segmentation, forecasting, test-and-control and causal inference, predictive modelling, and optimisation.
  • Work across client data estates, integrating syndicated and patient-level real-world data, clinical and trial data, engagement and CRM data, payer and access data, and operational and financial data, and resolve definitional conflicts.
  • Establish governed KPI, cohort, and business-rule definitions with clients and make them enforceable in code across every surface.
  • Convert analytical results into decisions through quality-checked, client-ready narratives and presentations, activation in workflows, dashboards, or products, and presentations to Director-level and more senior stakeholders.
  • Plan, review, and coach a team of 2–4, and quality-check all work before it reaches the client.
  • Own workstream status, risks, and dependencies with ZS leadership and the client project management office.
  • Contribute to proposals, points of view, and capability building through reusable assets and AI upskilling of CST talent.

Desired Qualifications

  • Life-sciences or healthcare analytics experience in any function, including commercial, medical, market access, clinical development, or operations.
  • Hands-on experience with real-world data such as claims, electronic health records, or registries, or with clinical/trial or engagement data.

About the Company

ZS

Global management consulting for sales optimization

ZS Associates helps organizations optimize their sales and marketing operations through data-driven analytics. The company uses mathematical models and rigorous analysis—originating from Kellogg professors in the 1980s—to size and structure sales teams, design territories, forecast markets, manage data, and design incentive compensation. Its services are delivered as consulting engagements that translate complex analytics into practical business decisions, such as how many salespeople a company needs, how to assign territories, and how to align incentives to drive performance. ZS differentiates itself with a deep, methodical focus on analytics, built from decades of experience and applied across industries beyond its pharmaceutical roots. The company’s goal is to help clients improve performance and return on investment by turning data into actionable sales and marketing solutions.

Company size
10,001+
Company stage
Not listed
Total funding
Not listed
Headquarters
Evanston, IL
Founded
1983

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