Data Scientist (Demand Forecasting)

Responsibilities

• Develop and enhance forecasting models using time series, statistical, and ML techniques for SKU- and branch-level demand prediction
• Partner with stakeholders to define requirements and identify relevant data sources
• Perform feature engineering to integrate seasonality, holidays, promotions, weather, and external drivers into models
• Conduct thorough data preprocessing, validation, and analysis
• Collaborate with data engineering and business teams to deploy models into production and automate recurring forecasts
• Track and evaluate model performance using MAE, RMSE, MAPE, and refine models based on results
• Prepare documentation, dashboards, and presentations for technical and business audiences

Requirements

• Bachelor’s or Master’s in Data Science, Statistics, Computer Science, Operations Research, or related field
• 4–5 years of experience in forecasting or analytics
• Expertise in time series models such as ARIMA/SARIMA, Exponential Smoothing, and Prophet
• Proficiency in machine learning models including XGBoost, LightGBM, Random Forest, and Neural Networks
• Proficiency in Python (pandas, scikit-learn, statsmodels, Prophet)
• Experience in feature engineering with holiday/event calendars and external data
• Familiarity with visualization tools like Tableau, Power BI, matplotlib, or seaborn

Preferred Qualifications

• Experience deploying forecasting models in production or cloud environments
• Knowledge of retail or restaurant analytics at SKU and store/branch level
• Exposure to big data tools, cloud platforms (AWS, GCP, Azure), and workflow automation
• Experience with deep learning models for time series such as LSTM or GRU