Data Scientist

ABOUT THE ROLE

We are looking for a Data Scientist with 3–5 years of hands-on experience and prior experience working for or delivering projects to a multinational grocery, supermarket, hypermarket, or general merchandise retailer.

The ideal candidate will have strong foundations in classical Data Science, statistics, and Machine Learning, with the ability to translate complex retail business problems into scalable, data-driven solutions.

This is a hands-on Data Science role. We are looking for someone with strong ML fundamentals rather than a profile focused primarily on GenAI/LLMs.

KEY RESPONSIBILITIES

• Develop and deploy machine learning models for real-world retail use cases such as demand forecasting, sales prediction, customer segmentation, pricing and promotion analytics, recommendation, inventory optimization, churn/retention, and basket analysis.

• Perform exploratory data analysis, feature engineering, model development, validation, and performance evaluation.

• Work with large-scale retail datasets including sales, customer, product, store, pricing, promotion, and inventory data.

• Apply statistical and machine learning techniques to solve complex and ambiguous business problems.

• Build production-quality Python-based Data Science solutions and collaborate with Data Engineers and ML Engineers on deployment and ML pipelines.

• Design experiments and evaluate model performance using appropriate statistical and business metrics.

• Work closely with business stakeholders to understand retail problems and translate them into analytical and ML solutions.

• Communicate analytical findings, model performance, and business recommendations clearly to technical and non-technical stakeholders.

• Identify opportunities to improve existing models, analytical processes, and business decision-making through advanced analytics.

REQUIRED SKILLS & EXPERIENCE

Retail Domain – Mandatory

• 3–5 years of professional experience in Data Science / Machine Learning.

• Prior experience working for or delivering projects to a multinational grocery, supermarket, hypermarket, or general merchandise retailer is mandatory.

• Strong understanding of retail business processes and data.

• Experience working with retail datasets related to sales, customers, products, stores, pricing, promotions, and/or inventory.

• Good understanding of retail use cases such as demand forecasting, merchandising, pricing, promotion, replenishment, customer analytics, and product/category analytics.

Data Science & Machine Learning

• Strong understanding of classical Data Science and Machine Learning fundamentals.

• Strong knowledge of regression, classification, clustering, decision trees, ensemble methods, gradient boosting, and time-series forecasting.

• Strong foundation in probability, statistics, hypothesis testing, feature engineering, model selection, and model validation.

• Strong hands-on programming experience in Python.

• Experience with Python libraries such as Pandas, NumPy, Scikit-learn, XGBoost, and/or LightGBM.

• Strong SQL skills and experience working with large datasets.

• Ability to select and evaluate models using appropriate statistical and business metrics.

GOOD TO HAVE

• Experience building and deploying ML models in production.

• Familiarity with ML pipelines, model monitoring, and MLOps practices.

• Experience with AWS, Azure, or GCP.

• Experience with Spark or other distributed data processing technologies.

• Exposure to GenAI/LLMs is a plus, but strong classical ML fundamentals are essential.

WHAT WE ARE LOOKING FOR

• Strong hands-on Data Science and Machine Learning practitioner.

• Prior experience in a multinational retail environment.

• Ability to understand retail business problems and translate them into effective ML solutions.

• Ability to independently take a problem from business understanding → data exploration → feature engineering → modeling → evaluation → productionization.

• Strong analytical and problem-solving skills.

• Comfortable working with large, complex, and imperfect real-world datasets.

• Strong communication skills and ability to work with both technical and business stakeholders.

EDUCATION

Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Data Science, Engineering, Economics, or a related quantitative discipline.