Data Scientist

Key Responsibilities

1. Data Collection & Preprocessing

• Gather structured and unstructured data from various sources (CRM, ERP, GCP BigQuery, APIs).
• Clean, transform, and normalize data for modeling.
• Ensure data quality and integrity for accurate forecasting.

2. Machine Learning Model Development

• Design and implement ML algorithms for predictive analytics (e.g., regression, time-series forecasting, ensemble methods).
• Build and optimize models for

  sales forecasting

  , demand prediction, and customer segmentation.
• Perform feature engineering to improve model accuracy.

3. Cloud Integration (GCP)

• Deploy ML models on

  Google Cloud AI Platform

  or Vertex AI.
• Utilize GCP services like

  BigQuery

  ,

  Cloud Storage

  ,

  Dataflow

  , and

  Pub/Sub

  for data processing and pipeline automation.
• Implement scalable solutions for real-time or batch predictions.

4. Sales Forecasting & Business Insights

• Develop accurate

  sales forecasting models

  using historical data, seasonality, and external factors.
• Provide insights on revenue trends, inventory planning, and pricing strategies.
• Collaborate with sales and finance teams to align forecasts with business goals.

5. Performance Monitoring & Optimization

• Track model performance using metrics like RMSE, MAPE, and accuracy.
• Continuously retrain models with new data to maintain relevance.
• Implement A/B testing for model improvements.

6. Visualization & Reporting

• Create dashboards and reports using

  Looker

  ,

  Tableau

  , or

  Google Data Studio

  .
• Present findings to stakeholders in a clear, actionable manner.

7. Collaboration & Communication

• Work closely with cross-functional teams (Sales, Marketing, Finance).
• Translate technical insights into business recommendations.
• Document processes and maintain reproducibility of models.