LEAD DATA SCIENTIST - Project forecasting

Job Description

We are looking for a highly skilled Forecasting Data Scientist with strong expertise in Python-based development, time-series forecasting, and applied statistical and machine learning techniques. The candidate will be responsible for designing, developing, and deploying scalable forecasting solutions to support business planning and decision-making across domains such as demand planning, pricing, supply chain, and revenue forecasting, working closely with business, analytics, and engineering teams.

Key Responsibilities

Design, develop, and maintain Python-based forecasting and data science solutions using industry best practices.

Develop and implement time-series forecasting models using statistical and machine learning techniques.

Apply forecasting approaches such as ARIMA, SARIMA, Prophet, and regression-based models.

Perform EDA, data preprocessing, and feature engineering for time-series and external drivers.

Build scalable forecasting pipelines across multiple hierarchies and granularities.

Measure, monitor, and improve forecast accuracy using appropriate evaluation metrics.

Deploy forecasting models into production environments and support downstream integrations.

Collaborate with business stakeholders to convert planning requirements into technical solutions.

Document assumptions, methodologies, and forecast outputs clearly.

Required Skills & Experience

Technical Skills

Strong proficiency in Python for forecasting and data science development.

Hands-on experience with statsmodels, Prophet, scikit-learn, or similar libraries.

Strong understanding of classical and ML-based forecasting techniques.

Experience with SQL for data extraction and analytical queries.

Experience building batch or automated forecasting pipelines.

Exposure to cloud platforms such as AWS, Azure, or GCP.

Experience using Git/GitHub for version control and collaboration.

Data & Forecasting Skills

Strong experience in time-series analysis, trend and seasonality modeling.

Experience incorporating promotions, pricing, holidays, and external regressors.

Understanding of forecast accuracy metrics, back-testing, and validation techniques.

Experience with forecast lifecycle management, monitoring, and re-training.

Ability to clearly communicate forecast assumptions and insights to stakeholders.

Experience & Education

68 years of experience in forecasting, data science, or applied analytics roles.

Bachelors or Masters degree in Statistics, Mathematics, Economics, Computer Science, or Data Science.

Experience mentoring junior analysts or data scientists is a plus.