We are a fast-growing retail analytics company working with leading FMCG brands and retailers across multiple markets. Our platform analyses promotional performance, pricing strategies, and customer engagement using large-scale retail datasets (EPOS, e-commerce, and flyer promotions).

We are building advanced analytics capabilities including Promotion impact measurement, Price elasticity modelling, Demand forecasting, Retailer & brand performance intelligence

Role Overview :

We are looking for a high-impact Data Scientist who can work end-to-end - from messy retail data to actionable business insights.

This role is not just modeling - it requires strong business understanding of retail + hands-on data engineering + ML modeling + insight generation.

Key Responsibilities :

- Perform data preparation, exploratory analysis, and feature engineering

- Analyse flyer promotions and retail campaigns (weekly/monthly) and build models for promotion uplift, baseline sales, cannibalization, and pricing intelligence (price elasticity, competitive pricing impact)

- Develop and deploy machine learning models for demand forecasting, price optimization, and promotion effectiveness using features like price, discount, promo duration, seasonality, and competitor signals

- Work with large-scale datasets and build data pipelines using SQL (BigQuery preferred) and Python, handling missing data, inconsistencies (packsize, SKU mapping), and time-series transformations

- Design robust analytical methodologies for baseline estimation, stock-out adjustments, and promo overlap handling, while collaborating with business stakeholders to validate assumptions

- Collaborate with engineers to move models from research into reliable, deployed workflows

Required Skills :

- Degree in mathematics, statistics, economics, or a related technical discipline

- 3+ years of experience in data analytics, data science, or a related analytical role

- Strong foundation in Python for data analysis and SQL (BigQuery or similar) for querying and analysing large datasets

- Solid understanding of analytics and modeling techniques including regression (especially log-log elasticity), time-series analysis, and exposure to causal inference

- Strong analytical, problem-solving, and troubleshooting skills

- Excellent communication skills with ability to engage stakeholders and leadership

Bonus (Strong Differentiator) :

If you've worked on :

- Price elasticity modeling

- Retail promotion analytics & optimization techniques

- Large-scale SQL + ML pipelines