| 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.Implementing data integration solutions using AWS Glue, AWS Lambda, Azure Data Factory, Azure Functions, GCP Functions, GCP Dataproc, Dataflow and other relevant services • Designing and managing data warehouses and data lakes, verifying data is organized and accessible • Monitoring and troubleshooting data pipelines, data warehouses and workflows to verify data quality, system reliability, performance and cost management • Implementing IAM roles and policies to manage access and permissions within AWS, Azure, GCP • Use AWS CloudFormation, Azure Resource Manager templates, Terraform for infrastructure as code (IaC) deployments • Use AWS, Azure and GCP DevOps services to build and deploy DevOps pipelines • Implementing data security practices using AWS, Azure, GCP, Snowflake or Databricks • Improving Cloud resources for cost, performance, and scalability • Proficiency in SQL and experience with relational databases • Proficient in programming languages such as Python, Java, or Scala • Familiarity with big data technologies like Hadoop, Spark, or Kafka is a plus • Experience with machine learning and data science workflows is a plus • Knowledge of data governance and data security practices • Demonstrating analytical, problem-solving, and communication skills • Having the ability to work independently and as part of a team in a fast-paced environment • Applying modern, cloud-based technology skills, ability to research emerging trends, analyst publications, and adoption of modern technologies in solution architectures • Collaborating and contributing as a team member: understanding personal and team roles, contributing to a positive working environment by building proven relationships with team members, proactively seeking guidance, clarification and feedback • Prioritizing and handling multiple tasks, researching and analyzing pertinent client, industry and technical matters, utilizing problem-solving skills, and communicating effectively in written and verbal formats to various audiences (including various levels of management and external clients) in a professional business environment • Coaching and collaborating with associates who assist with this work, including providing coaching, feedback and guidance on work performance. • Certification in Cloud Platforms [e.g., AWS Solutions Architect, AWS Data Engineer, Google Professional Cloud Architect, GCP Data Engineer Microsoft Azure Solutions Architect, Azure Data Engineer Associate, or Snowflake Core, Snowflake Databricks Data Engineer Associate] is a plus • Designing and implementing thorough data architecture strategies that meet the current and future business needs • Developing and documenting data models, data flow diagrams, and data architecture guidelines • Verifying data architecture is compliant with data governance and data security policies • Collaborating with business stakeholders to understand their data requirements and translate them into technical solutions • Evaluating and recommending new data technologies and tools to enhance data architecture • Building, maintaining, and improving ETL/ELT pipelines for data ingestion, processing, and storage across batch and real-time data processing • Building, maintaining, and improving Data Quality rules leveraging DQ tools and/or other ETL/ELT tools • Developing and deploying scalable data storage solutions using AWS, Azure and GCP services such as S3, Amazon RDS, DynamoDB, Azure Data Lake Storage, Azure Cosmos DB, Azure SQL DB, GCP Cloud Storage etc. |