Job Description

ABOUT THE ROLE

n

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.

n

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.

n

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.

n

KEY RESPONSIBILITIES

n

n
- 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.
n
- Perform exploratory data analysis, feature engineering, model development, validation, and performance evaluation.
n
- Work with large-scale retail datasets including sales, customer, product, store, pricing, promotion, and inventory data.
n
- Apply statistical and machine learning techniques to solve complex and ambiguous business problems.
n
- Build production-quality Python-based Data Science solutions and collaborate with Data Engineers and ML Engineers on deployment and ML pipelines.
n
- Design experiments and evaluate model performance using appropriate statistical and business metrics.
n
- Work closely with business stakeholders to understand retail problems and translate them into analytical and ML solutions.
n
- Communicate analytical findings,



model performance, and business recommendations clearly to technical and non-technical stakeholders.
n
- Identify opportunities to improve existing models, analytical processes, and business decision-making through advanced analytics.
n

n

REQUIRED SKILLS & EXPERIENCE

n

Retail Domain – Mandatory

n

n
- 3–5 years of professional experience in Data Science / Machine Learning.
n
- Prior experience working for or delivering projects to a multinational grocery, supermarket, hypermarket, or general merchandise retailer is mandatory.
n
- Strong understanding of retail business processes and data.
n
- Experience working with retail datasets related to sales, customers, products, stores, pricing, promotions, and/or inventory.
n
- Good understanding of retail use cases such as demand forecasting, merchandising, pricing, promotion, replenishment, customer analytics, and product/category analytics.
n

n

Data Science & Machine Learning

n

n
- Strong understanding of classical Data Science and Machine Learning fundamentals.
n
- Strong knowledge of regression, classification, clustering, decision trees, ensemble methods, gradient boosting, and time-series forecasting.
n
- Strong foundation in probability, statistics, hypothesis testing, feature engineering,



model selection, and model validation.
n
- Strong hands-on programming experience in Python.
n
- Experience with Python libraries such as Pandas, NumPy, Scikit-learn, XGBoost, and/or LightGBM.
n
- Strong SQL skills and experience working with large datasets.
n
- Ability to select and evaluate models using appropriate statistical and business metrics.
n

n

GOOD TO HAVE

n

n
- Experience building and deploying ML models in production.
n
- Familiarity with ML pipelines, model monitoring, and MLOps practices.
n
- Experience with AWS, Azure, or GCP.
n
- Experience with Spark or other distributed data processing technologies.
n
- Exposure to GenAI/LLMs is a plus, but strong classical ML fundamentals are essential.
n

n

WHAT WE ARE LOOKING FOR

n

n
- Strong hands-on Data Science and Machine Learning practitioner.
n
- Prior experience in a multinational retail setting.
n
- Ability to understand retail business problems and translate them into effective ML solutions.
n
- Ability to independently take a problem from business understanding → data exploration → feature engineering → modeling → evaluation → productionization.
n
- Strong analytical and problem-solving skills.
n
- Comfortable working with large, complex, and imperfect real-world datasets.
n
- Strong communication skills and ability to work with both technical and business stakeholders.
n

n

EDUCATION

n

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

📌 Data Scientist (Bengaluru)
🏢 Valiance Solutions
📍 Bengaluru