Responsibilities

1. Forecasting & Model Development

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.

2. Segmentation & Clustering

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.

3. Data Extraction & Feature Engineering

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.

4. Model Evaluation & Deployment

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.

5. Collaboration & Communication

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.