The 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 artificial intelligence to drive value and help transform Ford through proactive, actionable, evidence-based decision making. GDI&A delivers data-driven platforms and advanced analytics and AI/ML solutions for Ford Enterprise.

GDI&A - Industrial System Analytics (ISA) team is a trusted advisor providing data, advanced analytics/AI/ML, and insights to support, optimize, and transform areas across Ford enterprise like Quality, Manufacturing, Supply Chain / Purchasing, Program and Launch Management and Design Cost & Complexity.​We leverage cutting-edge technologies to improve vehicle launch timing, quality, and cost.

Responsibilities

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, and the ability to translate business needs into actionable solutions. Successful candidates will provide technical and thought leadership in advanced analytics, AI/ML, and people management.

The ideal candidate possesses a strong theoretical foundation in statistical analysis, machine learning, simulation, econometrics, optimization, data platforms, and artificial intelligence and product development. Given the dynamic nature of data analytics and AI, a commitment to continuous learning and upskilling and being hands-on is essential. This role requires a blend of technical expertise, hands-on experience, strategic thinking, and strong leadership skills.

As a Data Science Manager, you will

• lead a multi-disciplinary team of data scientists, machine learning engineers, product designers, data engineers, and software engineers through the entire lifecycle of complex analytical and AI/ML projects.

• provide technical leadership and be hands-on from concept to deployment.

• collaborate across GDI&A functions and partner with business stakeholders to drive strategic outcomes.

• lead, mentor, and inspire your team to deliver impactful results, while fostering an innovative, collaborative, and high-performing environment. 

   

Key Responsibilities:

• Strategic & Technical Leadership: Lead business engagement, apply and refine methodologies to deliver analytical solutions that help data driven decision making to reduce cost, improve quality and deliver value across Ford Enterprise.

• Data Improvement & Integration: Collaborate with business partners, GDI&A functional teams and Centers of Excellence to improve data quality and integrate data from diverse sources.

• Hands-on Expertise: Build, improve, and deploy Machine Learning & AI models to provide descriptive, predictive and prescriptive analytics.

• Team Leadership & Mentorship: Lead, mentor, and develop a high-performing team, fostering best practices in software development and data science.

• Technology Strategy: Develop and implement a roadmap for adopting and integrating new technologies (e.g., GCP).

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

Qualifications

Required:

• Master's degree (PhD preferred) in Engineering, Data Science, Computer Science, Statistics, Industrial Engineering, or a related field.

• 5+ years of hands-on experience applying supervised and unsupervised machine learning and AI/ML techniques field (OEM experience preferred).

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

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

• Experience with GCP and data related technologies (SQL, Spark, Hive).

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

• Experience working with Agile methodologies.

• Strong experience with Java/AngularJS/ReactJS.

Preferred:

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

• Familiarity with computationally intensive statistical methods, Software development life cycle, Agile, Jira, system architecture principles, PDO processes and software craftmanship.

• Google Digital Leader certification.