Principal Data Scientist

Agupdate; Dearborn (MI); Hybrid; USD 180,000 - 230,000; Full time

Job summary

Ford Motor Company in Dearborn, Michigan is seeking a Principal Data Scientist to design, build, and deploy end-to-end machine learning solutions for platform data challenges. The role blends home and on-site work, requiring residency within commuting distance.

You will lead data quality initiatives, build ML-driven monitoring, and drive R&D for data science capabilities across the product group. Collaboration with data engineers, architects, and privacy teams will be essential as you scale

Qualifications

• Extensive experience designing and deploying end-to-end ML solutions.
• Ability to analyze complex data quality issues and optimize data pipelines.
• Familiarity with orchestration, scheduling, and monitoring of data workflows.

Responsibilities

• Design, build, and deploy end-to-end ML algorithms for complex data challenges.
• Lead data quality initiatives across large data warehouses and lakes.
• Mentor engineers and collaborate with stakeholders to scale data platforms.

Skills

Machine learning

Data engineering liaison

Generative AI

Root-cause analysis

Tools

Docker

BigQuery

Cloud-native data services

Job description

Principal Data Scientist - positions offered by Ford Motor Company (Dearborn, Michigan). Note, this is a hybrid position whereby the employee will work both from home and from the aforementioned worksite. Hence, the employee must live within a reasonable commuting distance from the worksite. Design, build, and deploy end-to-end machine learning algorithms to tackle complex data challenges unique to platform. Developing identity resolution algorithms from scratch, refining for accuracy and performance at scale, and supporting production deployment. Lead efforts to identify, analyze, and resolve complex data quality issues across large-scale data warehouses and data lakes. Design and implement ML-driven monitoring tools for anomaly detection, data cleansing, standardization, and validation, ensuring high data integrity, reliability, and consistency from all sources. Contribute to developing internal tools, libraries, and automation scripts focused on enhancing data lineage, metadata management, and automated data quality checks. Building and managing Docker images and automating algorithms for efficient orchestration and scheduling. Apply advanced data science (statistical analysis, predictive modeling) to understand the performance, efficiency, and cost-effectiveness of large-scale data pipelines, ETL/ELT processes, and data storage solutions. Propose and implement data-driven improvements to optimize data flow, achieve sub-second latency, and enhance resource utilization. Use advanced analytical techniques for deep-dive root cause analysis on data discrepancies, performance bottlenecks, or system failures. Drive the strategic development of data science capabilities within the Product group, leading R&D efforts. Leverage Generative AI (LLMs) to identify problems and develop solutions within the data platform and data warehouse environment. Partner closely with data engineers and architects to design, optimize, and maintain scalable data structures and cloud-native data services. Manage the ingestion of data from various sources and formats into BigQuery and other data platforms. Implement robust transformation processes necessary for downstream analytical consumption. Act as a key liaison and project leader, mentoring data engineers and collaborating with global stakeholders. Translate complex business requirements into precise technical specifications for data solutions. Serve as a technical conduit between central privacy, product, and engineering teams for end-to-end system design. Create comprehensive documentation for data structures, data quality rules, and analytical findings related to the data platform. Share expertise, mentor junior team members, and foster best practices within the team and across the organization.

EOE.