Data Anchor

Join Ford’s mission to modernize automotive manufacturing through innovative data science, intelligent data pipelines, and AI/ML solutions. As a Data Anchor and AI/ML Engineer, you will be at the intersection of the digital and physical—architecting and deploying cutting-edge machine learning models and robust data ecosystems that bring predictive analytics, automation, and intelligence to the factory floor. You will help build the data platforms that power our plants, enabling real-time insights, optimized vehicle scheduling, and resilient operations that scale with the future of mobility. This is a great opportunity to drive the delivery of key enterprise objectives in building Ford’s flagship products. In this role, you will act as a technical anchor, working alongside a unique blend of software engineers, DevOps, automation, controls, and manufacturing business personnel. You will be responsible for ideating, building, and scaling billion-dollar data-driven ideas—leveraging streaming IoT telemetry, massive data lakes, and cutting-edge Generative AI—for the manufacturing of iconic Ford products. This is a rare opportunity to put your signature on how Ford uses data and AI to manufacture vehicles. - **AI/ML & Generative AI:** Architecting, developing, and deploying data leveraged through LLM models and toolsets such as Gemini Enterprise to build advanced generative AI solutions. - **Data Engineering & IIoT Pipelines:** Designing factory data standards streamed through Kafka and MQTT for IoT telemetry. - **Data Anchoring & Strategy:** Acting as the technical lead for data initiatives, defining data architecture standards, mentoring team members, and ensuring alignment with enterprise data strategies across hybrid environments. - **Cloud & Big Data Infrastructure:** Designing and maintaining robust data architectures with traditional SQL, BigQuery for enterprise data warehousing, MongoDB for flexible NoSQL document storage, and Hadoop ecosystems for distributed big data processing. - **Data Modernization:** Leading the migration and integration path from legacy on-premise systems (including legacy Hadoop clusters) to modern, cloud-native data platforms on GCP. - **Domain Expertise:** Gaining a deep understanding of the core functionality of Industrial Systems to drive data-driven optimizations and predictive modeling. - **Model & Data Monitoring:** Identifying and implementing monitoring solutions for data drift, model performance, and pipeline health using tools like Dynatrace, Splunk, and native cloud monitoring. - **Education:** Master’s degree in Data Science, Computer Science, Computer Engineering, Statistics, or a related highly quantitative field. - **Experience:** 5+ years of combined experience in Data Science, Data Engineering, and deploying AI/ML models into production environments. - **Technical Skills:** - Strong proficiency in programming languages for data and ML, specifically Python and SQL. - Deep expertise in big data processing frameworks (e.g., Hadoop, Apache Spark, Kafka). - Hands-on experience with streaming data and Industrial IoT protocols, specifically MQTT. - Advanced database experience across relational and non-relational systems, including deep knowledge of BigQuery. - Expertise in cloud platforms (GCP preferred) and leveraging advanced AI services like Gemini Enterprise and Vertex AI. - Exposure to machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn) and predictive modeling techniques. - Experience exposing ML models via RESTful APIs or microservices. - **Soft Skills:** - Proven ability to act as a "Data Anchor," guiding technical architecture and mentoring junior data scientists/engineers. - Excellent communication, interpersonal, and presentation skills, with the ability to translate complex concepts to non-technical business stakeholders. - Comfortable interacting with global plant IT and business customers to clarify needs and define data requirements. - A quick learner, adap…