Role overview
The Data Scientist is responsible for applying advanced analytics, statistical modeling, and machine learning techniques to extract insights from structured and unstructured data. The role partners with business and technology teams to solve complex problems, optimize decision-making, and enable data-driven strategies across the organization.

Key responsibilities

• Collect, clean, and analyze large datasets from multiple sources (internal systems, APIs, third-party data providers)

• Develop, train, and deploy predictive and prescriptive models using statistical, machine learning, and deep learning techniques

• Translate business problems into analytical frameworks and deliver actionable insights

• Design experiments (A/B testing, hypothesis testing) and measure outcomes to guide product and process improvements

• Communicate findings to stakeholders through clear storytelling, dashboards, and data visualizations

• Collaborate with data engineers to ensure reliable data pipelines and scalable model deployment

• Monitor model performance and retrain models to maintain accuracy and relevance

• Ensure data handling practices comply with privacy, governance, and regulatory requirements (GDPR, DPDP, HIPAA, etc.)

Required skills & qualifications

• Strong background in statistics, machine learning, and data science methodologies

• Proficiency in programming languages such as Python or R, with hands-on experience in ML libraries (scikit-learn, TensorFlow, PyTorch)

• Strong SQL skills and familiarity with data manipulation tools (Pandas, Spark, etc.)

• Experience with data visualization tools (Tableau, Power BI, matplotlib, seaborn, Looker)

• Understanding of cloud platforms (AWS, GCP, Azure) for data and ML model deployment

• Bachelors or Masters degree in Data Science, Computer Science, Statistics, Applied Mathematics, or a related field

• 6-8 years of relevant experience in data science or applied analytics

Preferred qualifications

• Exposure to big data ecosystems (Hadoop, Dataproc, BigQuery, Snowflake)

• Experience deploying models in production (MLOps practices, CI/CD for ML)

• Domain knowledge in Retail Supply Chain.

• Excellent communication skills to engage both technical and non-technical stakeholders

• Strong business acumen to connect data insights to strategic decisions