| Duties: | Responsible for the exploration, aggregation, transformation, and cleansing of vehicle data across a variety of disparate sources and platforms, ensuring data integrity and consistency at scale; design, implement, and continuously optimize advanced predictive machine learning models to drive accurate and actionable business forecasts, leveraging complex datasets; architect, develop, and maintain high-performance APIs in Python, ensuring seamless integration with diverse data ecosystems while adhering to best practices for scalability and security; support efforts to streamline and automate the data pipeline, encompassing data acquisition, feature engineering, model development, and deployment workflows, optimizing for performance, efficiency, and scalability; coordinate and execute complex ad-hoc data analysis tasks, providing rapid, data-driven insights for immediate business needs; provide mission-critical on-call support to ensure the continued operation of business production systems, troubleshooting and resolving issues with minimal impact to operations; utilize and apply knowledge of Python, SQL, Scikit-learn, XGBoost, Prophet, ARIMA, RESTful APIs, GCP, Terraform, Docker, VBA, and ODBC to complete assignments; translate complex analytical findings into clear, actionable insights; utilize cutting-edge Natural Language Processing (NLP) techniques to extract valuable insights from large volumes of unstructured text data, integrating AI-driven solutions to deliver sophisticated data analysis that directly impacts automotive operations and strategic initiatives; apply advanced statistical methodologies including Regression Analysis, Bayesian Inference, and machine learning-based forecasting techniques, to model and predict complex variables like market incentives, inventory management, sales forecasting, and operational performance; provide data-driven insights to optimize production strategies and facilitate high-level decision-making; leverage Cloud Computing platforms (primarily GCP) to architect and scale infrastructure for processing, storing, and analyzing massive automotive datasets; deploy data science solutions that integrate seamlessly with manufacturing and operational environments to drive efficiency, accuracy, and business intelligence; and present models and results to stakeholders, including business executives, to influence strategic decision-making. Education: Master’s – Data Science, Computer Science, Computer Engineering, Systems Engineering, or in a related field of study (will accept equivalent foreign degree); Training: None; Experience: One (1) year in the position above, as a Data Analyst, Data Engineer, as a Data Engineering Specialist, or in a related occupation; Other Requirements: Experience must include one (1) year use of all the following: Python, SQL, Scikit-learn, XGBoost, Prophet, ARIMA, RESTful APIs, GCP, Terraform, Docker, VBA, and ODBC.- Education: Doctoral, Master's or Bachelor's degree in a chemical, physical, biological or clinical laboratory science or medical technology from an accredited institution or transcripts showing 60 semester hours from an accredited institution indicating 24 semester hours of medical technology courses or 24 semester hours of science courses that include 6 semester hours in chemistry, 6 semester hours of biology, and 12 semester hours of chemistry, biology, or medical technology in any combination OR meet an alternate qualification route for Testing Personnel for High Complexity Testing as detailed in 493.1489.
- Years of Experience: Completion of a clinical laboratory training program approved or accredited by the ABHES, the CAHEA, or other organization approved by HHS OR meet an alternate experience route for High Complexity Testing Personnel as detailed in 493.1489.
|