We are looking for a Data Scientist with strong Machine Learning and Databricks expertise to join a California-based Clients Team. The ideal candidate will design, build, and deploy ML models on the Databricks platform, work with large-scale data pipelines, and collaborate with cross-functional teams to drive data-driven decision making.

Key Responsibilities:

Design, develop, and deploy machine learning models using Databricks (MLflow, Delta Lake)
Build and optimize data pipelines (ETL/ELT) using PySpark/Spark on Databricks
Perform exploratory data analysis, feature engineering, and model validation
Collaborate with data engineers, analysts, and business stakeholders to translate business problems into ML solutions
Deploy models into production and monitor performance (MLOps practices)
Work with structured and unstructured data at scale
Document processes, models, and findings clearly for technical and non-technical audiences

Required Skills:

4–6 years of experience as on Databricks Platform with Machine Learning Exp.
Strong hands-on experience with Databricks (notebooks, MLflow, Delta Lake, Unity Catalog)
Proficiency in Python (Pandas, NumPy, Scikit-learn) and PySpark
Experience with SQL and large-scale data processing
Solid grasp of ML algorithms (regression, classification, clustering, ensemble methods)
Experience with cloud platforms (Azure Databricks / AWS / GCP — confirm which one client uses)
Familiarity with MLOps tools, model deployment, and CI/CD for ML pipelines
Strong statistics and data visualization skills (Matplotlib, Seaborn, Power BI/Tableau a plus)

If you are interested, please share your updated resume, current location, work authorization, availability, and contact details.