Responsibilities

• Design, develop, and implement advanced time series forecasting models (e.g., ARIMA, Prophet, LSTM, XGBoost, etc.) tailored to different product categories and business needs.

• Evaluate and improve forecast accuracy by establishing robust metrics and conducting regular performance assessments throughout the sales cycle.

• Run what-if scenarios and simulations to assess the impact of various business conditions on forecast outcomes.

• Collaborate with the Senior Data Scientist to perform segmentation and clustering of product identifiers using unsupervised learning techniques (e.g., K-means, DBSCAN, hierarchical clustering).

• Analyze product behavior patterns to identify slow- and fast-moving items, and generate actionable insights for inventory and sales planning.

• Extract, clean, and transform data from multiple source systems (e.g., SQL databases, APIs, cloud storage) to support modeling and analysis.

• Engineer relevant features and variables to enhance model performance and interpretability.

• Conduct comparative analysis of forecasting methods across different segments, tuning parameters and optimizing performance.

• Work in close coordination with Data Engineers and cloud platform teams to ensure seamless deployment of models into production environments (e.g., AWS, Azure, GCP).

• Monitor deployed models for drift, accuracy, and performance, and implement retraining pipelines as needed.

• Partner with cross-functional stakeholders to understand business requirements and translate them into analytical solutions.

• Present findings, insights, and recommendations to both technical and non-technical audiences through reports, dashboards, and presentations.