Data Science Specialist

Location: Vasanthanagar

Job Title:

Data Scientist – Revenue Growth Management

Location:

Bangalore

Employment Type:

Full-time

Experience:

3–6 Years
Industry Focus: IT Services, Artificial Intelligence & Analytics

Position Summary
We are looking for a skilled Data Scientist – Revenue Growth Management (RGM) with 3–6 years of experience in Data Science, Advanced Analytics, or Machine Learning. The ideal candidate will have strong hands-on expertise in developing advanced analytics and machine learning solutions across pricing, promotion effectiveness, demand forecasting, and trade spend optimization.
The role will focus on developing scalable, data-driven solutions for Consumer Packaged Goods (CPG), FMCG, and retail businesses, leveraging commercial datasets and advanced statistical and machine learning techniques. The ideal candidate will combine strong data science and engineering capabilities with an understanding of key commercial levers such as price elasticity, promotional uplift, cannibalization, trade investment, revenue, and margin.
The candidate will work closely with sales, marketing, finance, category, and commercial teams to translate analytical outputs into actionable recommendations and business decisions.

Strategic   Responsibilities
Develop pricing and price-elasticity models across products, customers, channels, regions, and markets.
Build promotion effectiveness models to estimate baseline sales, incremental promotional uplift, cannibalization, halo impact, forward buying, and promotion ROI.
Develop demand and sales forecasting models to support commercial planning and revenue growth initiatives.
Create trade spend and promotional budget optimization solutions using statistical and mathematical optimization techniques.
Run scenario simulations to assess the impact of changes in price, discount depth, promotion timing, promotion frequency, and budget allocation.
Build scalable data pipelines and analytical workflows using Python, SQL, PySpark, and Databricks.
Work with diverse commercial datasets, including sales, pricing, promotion, distribution, inventory, product, customer, and market data.
Develop robust data science and machine learning models to support Revenue Growth Management initiatives.
Apply statistical modelling, regression, forecasting, feature engineering, and model validation techniques to solve complex commercial problems.
Translate model outputs and analytical findings into clear, actionable recommendations for sales, marketing, finance, category, and commercial stakeholders.
Manage the model lifecycle using MLflow, model registries, deployment, monitoring, and retraining practices.
Contribute to MLOps best practices, including model versioning, deployment automation, monitoring, and continuous improvement.
Optimize analytical solutions for scalability, performance, and business impact.
Collaborate with cross-functional business and technical teams to deliver production-ready data science solutions.
Stay updated with emerging trends in advanced analytics, machine learning, optimization, and AI technologies relevant to commercial and RGM use cases.

Required Experience

3–6 years of professional experience in Data Science, Advanced Analytics, Machine Learning, or a related field.
Strong hands-on experience with Python and SQL.
Working knowledge of PySpark and large-scale data processing.
Practical experience with Databricks, including notebooks, workflows/jobs, Delta Lake, and MLflow.
Strong understanding of Machine Learning, statistics, regression, forecasting, feature engineering, and model validation.
Experience in at least one of the following areas:
Pricing Analytics, Promotion Effectiveness, Demand Forecasting, Revenue Growth Management (RGM), Commercial Analytics, or Statistical/Mathematical Optimization.
Experience developing analytical models for business and commercial decision-making.
Strong understanding of data preparation, statistical modelling, model evaluation, and optimization techniques.
Experience working with structured commercial datasets such as pricing, sales, promotion, distribution, inventory, customer, product, and market data.
Understanding of MLOps practices, including model versioning,…