Job Description: Role Overview We are seeking a highly skilled Data Scientist with deep expertise in Databricks, Machine Learning, and AI-driven analytics to join our growing data organization. In this role, you will design, build, and deploy scalable models and data workflows that power enterprise insights, automation, and decision-making. You will work across engineering, analytics, and business teams to transform raw data into intelligent, production-ready solutions.

Key Responsibilities

Develop, train, and deploy machine learning models using Databricks notebooks, MLflow, and the Lakehouse architecture. Build scalable ETL/ELT pipelines leveraging Delta Lake, PySpark, and Databricks workflows. Implement AI/LLM-based solutions, including retrieval-augmented generation (RAG), vector search, and enterprise agent workflows.

Partner with data engineering to optimize datasets for analytics, modeling, and real-time inference. Conduct exploratory data analysis (EDA), feature engineering, and statistical modeling to uncover actionable insights. Use MLflow for experiment tracking, model versioning, and lifecycle management. Collaborate with business stakeholders to translate ambiguous problems into measurable, data-driven solutions.

Deploy models into production using Databricks Model Serving, serverless compute, or API endpoints. Ensure governance, security, and compliance using Unity Catalog and enterprise data standards. Continuously evaluate new AI/ML technologies and recommend improvements to the platform and modeling strategy.

Implement and uphold enterprise data governance standards, ensuring models and pipelines comply with regulatory, privacy, and audit requirements. Use Unity Catalog to manage secure, centralized governance for data, ML models, notebooks, and AI assets. Design and enforce Role-Based Access Control (RBAC) to ensure users only access data and models appropriate for their job functions.

Apply Attribute-Based Access Control (ABAC) for fine-grained, dynamic access decisions based on user attributes (e.g., department, region, clearance level) and data attributes (e.g., sensitivity, classification). Required

Qualifications

Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, or related field. 3–7+ years of experience building machine learning models in Python (Pandas, Scikit-learn, PySpark, TensorFlow, or PyTorch). Hands-on experience with Databricks, including notebooks, Delta Lake, MLflow, and Databricks SQL. Strong understanding of Lakehouse architecture, distributed computing, and scalable data processing.

Experience deploying ML models into production environments. Proficiency in SQL and Python for data manipulation and analysis. Familiarity with LLMs, embeddings, vector databases, or AI agent frameworks. Ability to communicate complex technical concepts to non-technical stakeholders.

Preferred Qualifications

Experience with Databricks Model Serving, Vector Search, or serverless warehouses. Background in NLP, deep learning, or generative AI. Experience integrating Databricks with SAP, Snowflake, or enterprise BI tools. Knowledge of MLOps best practices and CI/CD pipelines. Experience with cloud platforms (Azure, AWS, or GCP).

Experience with SAP BDC, BDC Connect What You’ll Bring A passion for solving complex problems with data and AI. Curiosity, creativity, and a strong desire to innovate. Ability to work in fast-paced, cross-functional environments. A mindset for building scalable, secure, and production-grade solutions. Position Type: Regular Additional Locations: Additional Information: Remote Status: Remote