Data Scientist

Job Title: Data Scientist – Databricks & ML Deployment Expert

Experience: 3-8 Years

About the Role We are seeking an experienced and versatile Data Scientist with a strong foundation in Databricks, PySpark, and end-to-end machine learning model development and deployment. This role will be instrumental in delivering advanced ML solutions across Retail and Automotive domains with specific use cases like CLV modeling, predictive maintenance, and time series forecasting.

Key Responsibilities • Build and scale machine learning models (Regression, Classification, Clustering) for real-world business problems • Work on Time Series Forecasting, Customer Lifetime Value (CLV) models, and Predictive Maintenance • Design and implement ML workflows using Databricks and PySpark • Automate ML and data workflows using time-based or event-driven triggers • Develop APIs (using Flask, FastAPI, or Django) to serve ML models as REST endpoints • Deploy and monitor ML models in production (must have experience with at least 1 deployments) • Collaborate with cross-functional teams including data engineering and DevOps for seamless CI/CD integration • Write efficient SQL queries for feature extraction, data cleaning, and model input preparation • Optimize model performance and pipeline efficiency (code and infrastructure level)

• Must-Have Skills • Databricks (Must Have) • PySpark (Must Have) • End-to-end experience in ML model development and deployment (at least 2 production deployments) • Strong in Regression, Classification, Clustering, and Time Series Forecasting • Knowledge of Medallion Architecture in data processing • Experience with API development (Flask/FastAPI/Django) • Hands-on with CI/CD pipelines and deployment automation • Proficient in SQL (Intermediate to Advanced) • Exposure to batch processing and workflow automation • Familiarity with ELT/ETL processes • Code or service-level optimization in ML pipelines Domain Exposure • Retail – CLV, Pricing Models, Demand Forecasting • Automotive – Predictive Maintenance, Time Series Forecasting Good to Have • Experience with MLflow, Docker, or Kubernetes • Understanding of cloud ecosystems (Azure, AWS, or GCP