[Hiring] Data Scientist @Cooper Standard

Role Description

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).

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

Remote Status

Remote