Data Science / AI Manager

Data Science / AI Manager

GDI&A - Data Science, Machine Learning and AI 

Product Manager 

 

Global Data Insights and Analytics (GDI&A) navigates Ford Motor Company through the disruptions of the information age, unleashing the power of Data, Advanced Analytics, and AI to drive value and help transform Ford Motor Company through proactive, actionable, evidence-based decision making. GDI&A delivers data-driven platforms, advanced analytics, and AI/ML solutions to support Ford’s Industrial System, Sales, Service, Marketing, Strategy, Finance, and Ford Credit.

 

We are seeking an initiative-taking and experienced candidate to lead and mentor a multi-skilled product team comprising of data scientists, machine learning engineers, product designers, and software engineers through the entire lifecycle of complex analytical and AI/ML data science products. This role demands strong technical expertise, hands-on experience with cutting-edge analytical tools (including AI/ML), strong understanding of software craftmanship & software development cycle, thought leadership and ability to translate business needs into analytical formulations, and a proven track record of success in creating impactful and insightful data science products. 

 

Ideal candidate for this role possesses a strong theoretical foundation in statistical analysis, machine learning, simulation, econometrics, optimization, data platforms, AI, and software design & development. Given the dynamic nature of data analytics and AI, a commitment to continuous learning, upskilling, and being hands-on is essential. This role requires a blend of technical expertise, hands-on experience, strategic thinking, business domain knowledge, thought leadership, and strong leadership skills. Domain knowledge in Ford processes, Product Development, Program and Launch management is an added advantage.

 

Key Responsibilities:

 

• Technical Leadership: Provide expert technical guidance and domain mentorship to data scientists, data engineers & software engineers in the product team, actively contribute to design discussions, algorithm selection, architecting scalable data science solutions (including pipelines, model training infrastructure, and deployment strategies), evaluating and selecting appropriate technologies, and maintain coding standards and best practices. Leverage ML Ops principles to ensure model accuracy, stability, and regulatory compliance through robust risk assessment and independent validation processes where required.

• Team Leadership & Mentorship: Lead, mentor, and develop a high-performing team, comprising of highly skilled data scientists, software engineers, and ML engineers, fostering best practices in software development and data science. Provide thought leadership and oversight on best practices, code reviews, and technical design. Collaborate closely with data engineers to seamlessly integrate data pipelines and infrastructure while ensuring data quality, consistency, and accuracy.

• Innovation: Contribute to the development and implementation of the team's innovation strategy, focusing on identifying opportunities for new product development, process improvement, and leveraging emerging technologies within AI/ML, advanced analytics, and software craftmanship. This includes staying informed about the latest research, exploring novel algorithms, evaluating new tools and techniques, and prioritizing user-centered design principles in all aspects of data science product development.

• Domain Expertise: Lead engagement with internal business partners, understand the business domain (e.g., product development, marketing, manufacturing, after-sales service, credit) for which data science products are being developed, converse in the language of business partners, efficiently translate business requirements into data & analytical problem formulations. Experience in Product development domain adds a distinct advantage.

• Communication: Clearly and concisely communicate technical information to both technical and non-technical audiences. Effectively communicate compelling data-driven recommendations to senior management and foster strong relationships with key business partners.

• GenAI Implementation: Lead the problem formulation, research, development, and implementation of GenAI to enhance existing solutions and create new innovative applications. This includes exploring and implementing various GenAI models (e.g., Large Language Models, Diffusion Models) and integrating them into data pipelines and workflows.

• Cross-functional Collaboration: Work effectively with various teams and stakeholders to achieve business objectives.

 

Qualifications:

 

Required:

• Master’s degree in engineering, Data Science, Computer Science, Statistics, Industrial Engineering, or a related field, Or, Bachelor's degree with 10+ years of experience in automotive industry and experience in Data Science/AI 
• 5+ years of hands-on experience applying supervised and unsupervised machine learning algorithms, deep learning architectures, and statistical modeling techniques (OEM experience preferred).
• 5+ years of leading a team of cross-functional technical experts.
• Proficiency in Python, along with experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn, XGBoost).
• Experience with cloud-based platforms (e.g., GCP) and data related technologies (e.g., SQL, Spark, Hive).
• Experience with various software development methodologies (Agile, DevOps), CI/CD pipelines, and testing frameworks. Proficiency in one of AngularJS, ReactJS, or any other front-end technologies.
• Strong technical & people leadership, communication, and collaboration skills.
• Understanding of business principles and critical thinking skills to translate business needs into impactful data science solutions.

 

Preferred:

• Experience with NLP, deep learning, neural network architectures, computer vision, reliability, survival analysis and anomaly detection techniques.
• Familiarity with computationally intensive statistical methods, Jira, system architecture principles.
• Google certifications on developing and deploying AI/ML models on GCP.

Key Responsibilities:

 

• Technical Leadership: Provide expert technical guidance and domain mentorship to data scientists, data engineers & software engineers in the product team, actively contribute to design discussions, algorithm selection, architecting scalable data science solutions (including pipelines, model training infrastructure, and deployment strategies), evaluating and selecting appropriate technologies, and maintain coding standards and best practices. Leverage ML Ops principles to ensure model accuracy, stability, and regulatory compliance through robust risk assessment and independent validation processes where required.

• Team Leadership & Mentorship: Lead, mentor, and develop a high-performing team, comprising of highly skilled data scientists, software engineers, and ML engineers, fostering best practices in software development and data science. Provide thought leadership and oversight on best practices, code reviews, and technical design. Collaborate closely with data engineers to seamlessly integrate data pipelines and infrastructure while ensuring data quality, consistency, and accuracy.

• Innovation: Contribute to the development and implementation of the team's innovation strategy, focusing on identifying opportunities for new product development, process improvement, and leveraging emerging technologies within AI/ML, advanced analytics, and software craftmanship. This includes staying informed about the latest research, exploring novel algorithms, evaluating new tools and techniques, and prioritizing user-centered design principles in all aspects of data science product development.

• Domain Expertise: Lead engagement with internal business partners, understand the business domain (e.g., marketing, manufacturing, after-sales service, credit) for which data science products are being developed, converse in the language of business partners, efficiently translate business requirements into data & analytical problem formulations.

• Communication: Clearly and concisely communicate technical information to both technical and non-technical audiences. Effectively communicate compelling data-driven recommendations to senior management and foster strong relationships with key business partners.

• GenAI Implementation: Lead the problem formulation, research, development, and implementation of GenAI to enhance existing solutions and create new innovative applications. This includes exploring and implementing various GenAI models (e.g., Large Language Models, Diffusion Models) and integrating them into data pipelines and workflows.

• Cross-functional Collaboration: Work effectively with various teams and stakeholders to achieve business objectives.

Required:

• Master’s degree in engineering, Data Science, Computer Science, Statistics, Industrial Engineering, or a related field, Or, Bachelor's degree with 10+ years of experience in automotive industry and experience in Data Science/AI 

• 5+ years of hands-on experience applying supervised and unsupervised machine learning algorithms, deep learning architectures, and statistical modeling techniques (OEM experience preferred).

• 5+ years of leading a team of cross-functional technical experts.

• Proficiency in Python, along with experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn, XGBoost).

• Experience with cloud-based platforms (e.g., GCP) and data related technologies (e.g., SQL, Spark, Hive).

• Experience with various software development methodologies (Agile, DevOps), CI/CD pipelines, and testing frameworks. Proficiency in one of AngularJS, ReactJS, or any other front-end technologies.

• Strong technical & people leadership, communication, and collaboration skills.

• Understanding of business principles and critical thinking skills to translate business needs into impactful data science solutions.

 

Preferred:

• Experience with NLP, deep learning, neural network architectures, computer vision, reliability, survival analysis and anomaly detection techniques.

• Familiarity with computationally intensive statistical methods, Jira, system architecture principles.

• Google certifications on developing and deploying AI/ML models on GCP.

Experience Level

Mid Level