Role and Responsibility:

Data Science And Marketing:

· Design and apply appropriate analytical and measurement approaches for marketing initiatives, including experimentation, matched-market analysis, synthetic control, difference-in-differences, uplift modelling, and time-series methods, based on the business question and available data.

· Develop incrementality and marketing-effectiveness analyses across retail media, CRM, and digital channels, with clear assumptions, validation steps, and interpretation of limitations.

· Analyze campaign and retail media performance to identify trends and opportunities related to sales, return on advertising spend, traffic, conversion, and spend efficiency.

· Assess potential halo and cross-channel effects and help stakeholders understand the difference between platform-reported results and estimated incremental impact.

· Conduct forecasting, simulations, segmentation, customer journey analysis, and focused statistical investigations to support priority business decisions.

Profitability, ROI and Business Planning:

· Develop and maintain profitability and ROI analyses at relevant levels such as retailer, campaign, category, product, SKU, or customer, using available revenue, margin, return, fulfilment, trade, and media-cost inputs.

· Partner with Finance and business teams to align definitions, reconcile results, and make profitability and return measures transparent and decision-ready.

· Build scenario analyses and forecasts to support budget planning, investment choices, demand expectations, and performance outlooks.

· Evaluate forecast and model performance using suitable back-testing, reasonability checks, and documented criteria.

Data Analysis, Engineering and Automation:

· Acquire, integrate, cleanse, normalize, and analyze data from retail media, CRM, digital analytics, commerce, and enterprise sources.

· Develop reusable analytical datasets and automated workflows using SQL and Python or R, applying cloud such as Fabrics, Azure, Databricks or distributed-processing tools where appropriate.

· Consolidate cross-retailer performance data into consistent reporting structures with shared taxonomy, KPI definitions, and reconciliation controls.

· Own and executive the transition of recurring manual reporting and analysis toward scalable Power BI solutions and governed data models in partnership with data engineering and platform teams.

Reporting, Insights and Business Partnership:

· Develop weekly, monthly, and quarterly performance narratives that explain what happened, why it happened, and the recommended next actions.

· Perform root-cause analysis of movements in marketing and commercial KPIs, quantify key drivers where feasible, and clearly distinguish evidence from assumptions.





· Work with stakeholders to formulate business questions, translate requirements into analytical specifications, and deliver findings in a form that supports action.

· Present analytical outcomes to technical and non-technical stakeholders, adapting the level of detail for working teams, business sponsors, and leadership forums.

· Partner with digital and marketing-technology teams to define measurement requirements, validate data collection and tagging coverage, and help resolve analytics data issues.

Project Ownership and Technical Contribution:

· Lead analytics initiatives from problem framing and feasibility assessment through planning, execution, validation, delivery, handover, and adoption.

· Identify and shape analytics opportunities with business sponsors by clarifying expected outcomes, data requirements, dependencies, effort, risks, and success measures.

· Maintain transparent documentation of methods, assumptions, code, validation evidence, decisions, and reusable knowledge assets.

· Provide methodological guidance, constructive review, and knowledge sharing to analysts and project contributors.

· Collaborate with vendors and implementation partners when required, supporting evaluation, delivery reviews, validation, and knowledge transfer.

Organization Relationships:

· Works closely with the Head of Analytics on priorities, project direction, and delivery outcomes.

· Partners with Retail Media, Brand and Performance Marketing, CRM, eCommerce, Finance, Commercial, Sales, and Digital teams on analytics and measurement priorities.

· Works with Data Engineering, IT, and enterprise platform teams on data access, integration, solution readiness, and production support.

· Collaborates with front-end development, back-end development, QA, production, content, strategy, design, and product teams on measurement requirements for digital initiatives and campaigns.

· Engages with enterprise data and governance functions on shared data assets, access, quality, privacy, and documentation.

Applied AI and Innovation:

· Explore practical applications of generative and agentic AI in marketing analytics, including data validation, anomaly investigation, knowledge retrieval, unstructured-text analysis, and insight generation.

· Develop and evaluate proofs of concept where there is a defined business need, considering output quality, risk, cost, privacy, maintainability, and appropriate human review.





External Business Relationships:

· Works with retailer and media-platform contacts on data availability, taxonomy, reporting, and measurement-related topics.

· Collaborates with agencies, implementation partners, and technology or data vendors on assigned project activities, delivery validation, and issue resolution.

· Supports the evaluation and onboarding of external data or analytical capabilities in accordance with applicable Kohler processes.

Core Competencies and Knowledge required:

· Bachelor's or Master's degree in Statistics, Econometrics, Mathematics, Computer Science, Data Science, Engineering, Economics, Information Management, or a related quantitative discipline.

· Typically, 7+ years of relevant experience in data science, advanced analytics, marketing analytics, business analytics, or data engineering, including ownership of complex analytical initiatives.

· Strong applied knowledge of regression, hypothesis testing, experimental design, time-series analysis, forecasting, segmentation, and model evaluation.

· Practical understanding of causal inference and incrementality measurement, with the ability to select methods appropriate to the business question and available data.

· Advanced SQL and hands-on proficiency in Python or R for data preparation, analysis, modelling, automation, and reproducible analytical workflows.

· Working knowledge of data pipelines, data modelling, ETL/ELT practices, quality controls, and cloud analytics environments.

· Experience with Power BI and advanced Excel, including the ability to develop reliable reporting datasets, metric definitions, and decision-focused outputs.

· Familiarity with marketing, digital, CRM, commerce, or enterprise platforms such as Adobe Analytics or GA4, Salesforce, SFMC, SAP, and retail media reporting sources.

· Ability to communicate complex analytical concepts clearly and influence senior technical and non-technical stakeholders through evidence-based recommendations.

· Ability to work effectively across multicultural and distributed teams, manage ambiguity and competing priorities, and deliver against agreed timelines.

Good To Have:

· Understanding of generative AI, large language models, retrieval-augmented generation, agentic workflows, evaluation approaches, and responsible AI practices.

· Exposure to Azure, Databricks, MLflow, or comparable cloud analytics and model-lifecycle tools.

· Knowledge of media mix modelling, attribution, digital measurement strategy, customer lifetime value, or identity resolution.

· Experience in retail media, consumer products, eCommerce, omnichannel retail, or a global marketing analytics environment.

· Familiarity with Git, APIs, workflow orchestration, CI/CD, or MLOps concepts.

📌 DM- Digital Marketing & Analytics (Pune)
🏢 Kohler
📍 Pune