Principal Data Scientist

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.

Ph.D. in Computer Science, Statistics, Mathematics or a related field and 5 years of experience in the job offered or a related occupation. 5 years of experience with each of the following skills is required: 1. Data Science or advanced data engineering. 2. Python development (including Pandas, NumPy, and Scikit-learn) for data manipulation, analysis, and scripting. 3. Using Machine Learning frameworks and libraries including at least 5 of the following: Pyspark, BigQuery ML, Pandas, Scipy-Weave, Multiprocessing, Graph ML libraries, Neo4j, NLTK, or MatplotLib. 4. Designing, implementing, and deploying machine learning models for complex data problems including anomaly detection or predictive maintenance within data systems, including algorithm fine-tuning for performance and accuracy at scale. 5. Researching, evaluating, and integrating new ML capabilities, covering both foundational and cutting-edge approaches. 3 years of experience with the following skill is required: 1. Using SQL skills for querying, manipulating, and optimizing complex datasets, including query tuning., Ph.D. in Computer Science, Statistics, Mathematics or a related field and 5 years of experience in the job offered or a related occupation. 5 years of experience with each of the following skills is required: 1. Data Science or advanced data engineering. 2. Python development (including Pandas, NumPy, and Scikit-learn) for data manipulation, analysis, and scripting. 3. Using Machine Learning frameworks and libraries including at least 5 of the following: Pyspark, BigQuery ML, Pandas, Scipy-Weave, Multiprocessing, Graph ML libraries, Neo4j, NLTK, or MatplotLib. 4. Designing, implementing, and deploying machine learning models for complex data problems including anomaly detection or predictive maintenance within data systems, including algorithm fine-tuning for performance and accuracy at scale. 5. Researching, evaluating, and integrating new ML capabilities, covering both foundational and cutting-edge approaches. 3 years of experience with the following skill is required: 1. Using SQL skills for querying, manipulating, and optimizing complex datasets, including query tuning.