Databricks Data Scientist

Role: Databricks Data Scientist Location: Indianapolis, IN - onsite Duration: 6 months Desirable Skills: Keyword: ~AWS| Python~ Skills: Digital : Amazon Web Service(AWS) Cloud Computing~Digital : Databricks Experience Required: 10 & Above Job Description: We are seeking a Databricks Data Scientist with strong experience in Databricks Lakehouse, advanced analytics, and Genie (AI/BI) to design, build, and deploy scalable data science and AI solutions. This role will focus on transforming enterprise data into actionable insights using machine learning, natural language analytics, and self-service BI powered by Databricks Genie Key Responsibilities Data Science & Machine LearningDesign, develop, and deploy machine learning models using Databricks (MLflow, Spark ML, Python).Implement end-to-end ML pipelines (data ingestion → training → deployment → monitoring).Collaborate with data engineers to ensure reliable, high-quality datasets in the Lakehouse. Databricks & Lakehouse ArchitectureLeverage Databricks Lakehouse (Delta Lake, Unity Catalog) for scalable analytics.Optimize Spark jobs for performance and cost efficiency.Apply best practices for data governance, lineage, and security. Genie (AI/BI & Natural Language Analytics)Configure and enable Databricks Genie for self-service analytics.Design semantic layers and curated datasets optimized for natural language queries.Partner with business stakeholders to translate questions into Genie-enabled insights. Business Enablement & CollaborationWork closely with product owners, analysts, and business leaders to identify high-value use cases.Communicate complex analytical results in a clear, business-friendly manner. Required Qualifications:Bachelor's or master's degree in data science, Computer Science, Statistics, Engineering, or a related field.4+ years of experience in data science or advanced analytics.Hands-on experience with Databricks and Apache Spark.Strong programming skills in Python (PySpark, Pandas, NumPy, Scikit-learn).Experience building and deploying ML models in production.Solid understanding of SQL and data modeling.Experience with MLflow, model lifecycle management, and experimentation.