Associate Director - Analytics

About PharmEasy

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Head of Analytics — PharmEasy (Consumer)

About the role

You will own the analytics and data science agenda for PharmEasy's consumer business — spanning demand, growth, retention, cross-sell/upsell, and customer experience. This is a charter heavy on recommendations, personalization, and applied data science, where analytics is expected to directly move repeat rate, attach rate, conversion, and acquisition efficiency. You'll lead a team of 15+ analysts and data scientists organized across these charters, act as the primary analytics thought partner to PharmEasy's business and product leadership, and set the technical and decision-science bar for the function. (SCM and fulfillment analytics are owned by a separate team and are out of scope for this role.)

What you'll own

Business partnering & strategy

Act as the primary analytics partner to PharmEasy's senior business and product leaders, proactively surfacing where data can move the P&L.

Translate ambiguous business problems into scoped roadmaps with clear success metrics and ROI framing tied to commerce outcomes — repeat rate, x-sell attach, conversion, AOV, CAC efficiency, retention.

Influence strategic decisions through data-backed recommendations to CXO-level, cross-functional audiences.

Embed analytics into core planning cycles across Growth, Product, Marketing, and Engineering.

Recommendations & personalization (core to this role)

Own the analytics and data-science behind PharmEasy's recommendation and personalization systems — candidate generation, ranking, and the business-rule layer.

Drive substitutes/complements logic, cold-start handling, and x-sell/upsell models, with rigorous incrementality measurement rather than vanity CTR.

Ensure recommendation logic respects Rx/OTC and regulatory constraints — consumer-health personalization is not horizontal e-commerce.

Growth, demand & marketing measurement

Own measurement for the demand and growth charters: marketing attribution, media-mix modelling, channel efficiency, and CAC/LTV economics.

Partner with Growth on funnel diagnosis, conversion levers, and demand-shaping.

Retention & CX analytics

Drive retention analytics for a refill-heavy consumer base — cohort behaviour, churn prediction, and uplift modelling to target persuadable users.

Own CX analytics: NPS driver analysis, service and experience metrics, and their link to retention and LTV.

Experimentation & causal inference

Build a culture of experimentation across business teams, with discipline on power/MDE, holdouts, variance reduction, and sequential-testing pitfalls.

Establish credible causal measurement where RCTs aren't feasible — diff-in-diff, matching, synthetic control, uplift modelling — and hold the bar on honest impact attribution.

Team, org & leadership

Lead, mentor, and grow a 15+ person team, including managing team leads/managers, across the charters above.

Design the org — embedded pods vs. central, and the right analyst / data scientist / analytics-engineer mix per charter.

Define goals, run performance management, build structured career paths and succession, and recruit to the bar.

Prioritize ruthlessly across competing charters; own portfolio management and on-time delivery against business commitments.

Champion best practices in analytics engineering, code review, documentation, and knowledge sharing.

Technical leadership

Set the technical bar across the stack — data ingestion through modelling, validation, and production deployment. Technically credible enough to challenge architecture and modelling choices, not to write daily production code.

Guide development of advanced ML/statistical work: regression, classification, clustering, time series, causal inference, NLP, and recommendation systems.

Own the KPI-definition and metrics layer — the single source of truth — while the team builds and maintains dashboards.

Ensure rigorous standards for data quality, validation, and model monitoring in production.

Evaluate emerging methods — including LLM-powered analytics — to keep raising the team's capability bar.

What you'll need

• 7–15+ years in analytics / data science, including 5+ years leading teams, with experience managing other leads or managers.
• Bachelor's or Master's in a quantitative discipline (Engineering, Statistics, Applied Math, CS, Data Science, or similar).
• Strong applied foundation in Python and SQL — deep enough to review work, set standards, and call out flawed analysis. (Day-to-day hands-on coding is not the expectation at this level.)
• Demonstrated production experience with recommendation systems and/or causal measurement at scale — not just classical ML.
• Strong statistical foundations: hypothesis testing, regression, Bayesian inference, experimentation, and causal inference.
• Proven track record of analytics/ML in production tied to measurable business impact.
• Excellent stakeholder management — able to distil technical complexity into clear narratives for senior, non-technical audiences.

Preferred

• Consumer internet / e-commerce / healthtech background, with high-velocity transactional data at 10M+ user scale.
• Familiarity with cloud data platforms (BigQuery, Snowflake, Redshift) and orchestration (Airflow, dbt).

Why this role

PharmEasy runs one of India's largest consumer-health transaction footprints, giving this team unusually rich behavioural and commerce data. The charters here map directly to P&L outcomes, with CXO visibility and the mandate — and the scale — to make recommendation and retention systems move real numbers. [Add specifics: order volume, MAU, reporting line.]