Associate Director - Analytics

Purplle.com; Mumbai; On-site; INR 400,000 - 700,000; Full time

Job summary

Purplle.com is seeking an experienced Analytics Leader to own the data layer that turns warehouse data into actionable decisions across commerce, CPG brands, supply chain, and stores. You will build the semantic layer, define metrics, and enable AI-assisted analysis for multiple functions.

You will lead a team, define the analytics roadmap, and establish robust experimentation and governance to ensure trusted insights and self-serve capabilities across the organization.

Qualifications

• 8+ years in analytics, BI, or data science; 3+ years leading teams.
• Expert SQL; strong Python for analysis and modelling.
• Hands-on with warehousing, dbt-style transformation, and a semantic layer.

Responsibilities

• Define and execute analytics roadmap across commerce, discovery, CPG, supply chain, stores and marketing.
• Own the experimentation standard: power analysis, guardrail metrics, quasi-experimental methods.
• Lead AI leverage for exploration, SQL generation, and first-pass analysis.
• Provide executive counsel with analytical recommendations and ownership.

Skills

SQL

Python

Analytics leadership

Data visualization

Semantics layer

Metrics store

Tools

dbt

Job description

Job Description

Purplle is two businesses under one roof. A commerce platform — app, web, and ~250 stores serving 15M+ monthly users. And a CPG house of brands that sells on Purplle, on other marketplaces, in quick commerce, and in general and modern trade. Both run on a data stack that is mid-rebuild.

You’ll own the layer that turns our warehouse into decisions — metric definitions, the semantic layer that agents and decision engines query, and the predictive systems that tell us what is about to happen rather than what already did. Across every function: commerce, category and merchandising, CPG brand P&Ls, supply chain, stores, retail media, marketing, and finance. No function is a second-class consumer.

What you’re building

• Medallion, with a clear line of ownership. Data Engineering owns bronze and silver — ingestion, contracts, schema validation, freshness SLAs. You own gold: the curated marts, conformed dimensions, and business logic that everything downstream depends on. One definition of a metric, one place it lives, and a hard rule that nothing consumes silver directly.
• A semantic layer that machines consume, not just humans. Metric and entity definitions as versioned code, compiling into three consumption paths:
• Natural-language analytics — dynamic resolution for conversational querying, so a category manager, a brand lead, or a store cluster head gets an answer without an analyst in the loop
• Typed contracts for decision engines and agents — pricing, replenishment, assortment, personalization and spend-allocation systems read metrics through a stable API, not hand-rolled SQL that drifts
• Reference definitions for the ML workbench — features and targets built off the same logic that reports use, so model outputs and dashboards never disagree
• This is the highest-leverage asset you will own. Its quality determines whether our agent layer is trustworthy or noisy.
• Self-serve as the default, with governance underneath. Business teams build their own views on our internal analytics surface. Your team's job is to make that safe and fast: certified metrics, sensible defaults, row-level access, and clear deprecation of the long tail of stale dashboards. Success is measured by how few questions reach your team, not how many you answer.
• Metrics and predictive inferencing. Define the metric tree — from GMV and contribution margin down to the operational drivers each pod actually controls, with paired counter-metrics so nothing gets gamed. Every function gets its own branch and its own owner.

What you own day to day

• Roadmap. Define and execute one analytics roadmap covering commerce and discovery, category and merchandising, the CPG brand portfolio, supply chain and quick commerce, retail stores, retail media, marketing, and finance. Allocate your team's capacity to where the margin is, not to whoever asks loudest.
• Causal rigour. Own the experimentation standard — power analysis, guardrail metrics, and quasi-experimental methods where clean A/B isn't possible: geo tests, staggered store rollouts, price and promo changes, media holdouts, packaging and formulation changes. Kill naive pre-post reads wherever they appear.
• AI leverage. Your team uses agents for exploration, SQL generation, and first-pass analysis as a default, not a novelty. You set the norms for where a human must stay in the loop.
• Executive counsel. Be the person leadership calls before a decision, not after. Translate ambiguity into an analytical question, then into a recommendation with an owner and a number attached.

Year one, measured

• Gold layer live across core domains — customer, SKU, store, brand, supply — with certified metric definitions replacing duplicate logic
• Semantic layer serving both conversational analytics and at least two production decision systems
• Majority of recurring business questions answered self-serve, with the legacy dashboard estate materially reduced
• Experimentation standard adopted org-wide, plus a documented list of decisions it changed
• At least two predictive systems in production, in different functions, with tracked business impact rather than model metrics

What we look for

• 8+ years in analytics, BI, or data science, including 3+ years leading teams
• E-commerce, D2C, CPG, retail, or consumer tech — you have carried a number, not just reported one
• Range across functions. You can hold a conversation about fill rate and one about ROAS in the same afternoon, and you know which of the two is currently costing more money
• Expert SQL; strong Python for analysis, modelling, and light tooling
• Hands-on with modern warehousing, dbt-style transformation, and a semantic layer or metrics store
• Track record moving a team from reactive reporting to predictive and automated decisioning — including the political work of retiring what people were attached to

About Company

Founded in 2011, Purplle has emerged as one of India’s premier omnichannel beauty destinations, redefining the way millions shop for beauty. With 1,000+ brands, 60,000+ products, and over 7 million monthly active users, Purplle has built a powerhouse platform that seamlessly blends online and offline experiences.

Expanding its footprint in 2022, Purplle introduced 6,000+ offline touchpoints and launched 100+ stores, strengthening its presence beyond digital. Beyond hosting third-party brands, Purplle has successfully scaled its own D2C powerhouses—FACES CANADA, Good Vibes, Carmesi, Purplle, and NY Bae—offering trend-driven, high‑quality beauty essentials.

What sets Purplle apart is its technology driven hyper-personalized shopping experience. By curating detailed user personas, enabling virtual makeup trials, and delivering tailored product recommendations based on personality, search intent, and purchase behavior, Purplle ensures a unique, customer‑first approach.

In 2022, Purplle achieved unicorn status, becoming India’s 102nd unicorn, backed by an esteemed group of investors including ADIA, Kedaara, Premji Invest, Sequoia Capital India, JSW Ventures, Goldman Sachs, Verlinvest, Blume Ventures, and Paramark Ventures.

With a 3,000+ strong team and an unstoppable vision, Purplle is set to lead the charge in India’s booming beauty landscape, revolutionizing the way the nation experiences beauty.