Senior Data Scientist (Local to Charlotte NC)
Ready to drive the future?
As part of the global Bertrandt Group, our team of innovators tackles cutting-edge projects across ADAS, Autonomous Driving, Electric Mobility, and Manufacturing Support, transforming complex issues into sustainable, connected solutions.
With the strength of a global network of over 14,500 colleagues in 50+ locations, Bertrandt US combines deep expertise in Electronics, Product Engineering, Physical, and Production & After Sales. Join us in engineering tomorrow’s mobility today.
General Benefits:
- Complete and comprehensive benefits package including Med/Dent/Vision
- Employer paid STD/LTD/Life
- 401k Retirement program
- Generous paid vacation/sick/holidays
- Creativity encouraged in a fun, friendly work environment
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Data Engineering & Data Processing
• Design and develop scalable ETL/ELT pipelines for ingesting, transforming, and processing structured and unstructured data.
• Build and optimize data pipelines using Databricks, Spark, SQL, and cloud-native AWS services.
• Implement data quality, validation, lineage, and monitoring processes.
• Support medallion/lakehouse architecture patterns including bronze, silver, and gold data layers.
• Develop data pipelines to support AI/ML, GenAI, and RAG workloads, including document ingestion and embedding generation workflows.
Machine Learning & Modeling
• Design and implement scalable ML models for classification, regression, clustering, forecasting, and recommendation systems.
• Apply advanced techniques including deep learning, ensemble learning, NLP, Generative AI, and LLM-based solutions where applicable.
• Conduct model evaluation, tuning, validation, and performance optimization using industry best practices.
• Develop and train models within Databricks ML and/or AWS SageMaker leveraging distributed computing and scalable cloud infrastructure.
• Build reusable feature engineering and model training pipelines.
• Develop Retrieval-Augmented Generation (RAG) solutions integrating LLMs with enterprise knowledge sources and vector databases.
Cloud & MLOps
• Deploy and manage ML and GenAI models using AWS SageMaker and Databricks, including endpoint configuration, monitoring, and retraining workflows.
• Utilize Databricks MLflow for experiment tracking, model registry, and deployment automation.
• Implement and support vector database solutions for semantic search and RAG architecture.
• Collaborate with DevOps and platform teams to implement CI/CD pipelines for ML, GenAI, and data workloads.
• Automate operational workflows and optimize cloud resource utilization, scalability, reliability, and security.
Deliverables
• Production-ready ML and GenAI solutions with supporting technical documentation.
• Scalable ETL/ELT pipelines and curated datasets.
• End-to-end Databricks notebooks, jobs, and workflows.
• Feature engineering pipelines and reusable ML components.
• RAG pipelines integrated with vector databases and enterprise knowledge sources.
• Weekly status reports and participation in Agile sprint ceremonies.