Data Scientist

Job Title: Data Scientist Retail / Loyalty / CRM Analytics

Experience: 35 Years

Location: India (Pune/Bangalore/Chennai/Gurugram)

Employment Type: Full-time

About the Role

We are looking for a Data Scientist with strong experience in Retail, Loyalty, or CRM analytics to drive customer growth and marketing effectiveness through data-driven insights and predictive models. The ideal candidate is comfortable working with large customer datasets (transactions, loyalty activity, digital behavior, campaign touchpoints) and can translate business problems into production-ready analytics and ML solutions.

You will partner with business and engineering teams to build models and measurement frameworks that improve acquisition, retention, engagement, personalization, and customer lifetime value.

Key Responsibilities

Develop predictive models for churn, propensity (buy/upsell), next-best-action, CLV/LTV, and customer segmentation.

Build and validate customer cohorts, loyalty performance tracking, and campaign effectiveness analytics.

Design uplift / incremental impact approaches where applicable (attribution model, test/control, A/B testing, causal methods).

Create features from multi-source customer data: POS/e-commerce transactions, loyalty events, web/app behavior, CRM interactions.

Collaborate with data engineering to operationalize models in scalable pipelines (batch/near real-time as needed).

Communicate insights and recommendations clearly to stakeholders (marketing, CRM, product, and leadership).

Ensure analytics solutions follow best practices around data quality, governance, and privacy (PII handling).

Required Skills & Qualifications

35 years of professional experience as a Data Scientist in Retail / Loyalty / CRM / Customer Analytics domains.

Strong proficiency in Python (pandas, NumPy, scikit-learn) and SQL.

Hands-on experience with supervised ML (classification/regression), segmentation/clustering, and model evaluation.

Strong understanding of customer/marketing metrics: retention, churn, repeat rate, RFM, CAC, conversion, AOV, basket analysis, LTV.

Experience with experimentation concepts: A/B testing, holdouts, bias checks, measurement discipline.

Ability to translate business problems into well-scoped analytical approaches and deliverables.

Strong communication skills with ability to explain methods, assumptions, and trade-offs to non-technical audiences.

Good to Have (Bonus)

GenAI / LLM experience: text analytics on customer feedback, summarization, classification, embeddings, retrieval (RAG) for CRM insights.

Experience with big data tools: Spark / Databricks, cloud platforms (Azure/AWS/GCP), and MLOps basics.

Familiarity with CRM/MarTech ecosystems (e.g., CDPs, marketing automation tools) and campaign orchestration concepts.

Recommender systems exposure (ranking, personalization, similarity models).

Knowledge of privacy/security practices related to customer data (masking, access controls, GDPR-like principles).

What Were Looking For

Strong customer-growth mindset: ability to connect analytics work to real business outcomes.

High ownership: can take problems from ambiguity analysis/model recommendation deployment-ready output.

Practical modeling approach: balances rigor with speed, clarity, and stakeholder usability.

Collaborative working style with strong documentation and repeatable workflows.