Job Description

The Global Data Insights and Analytics (GDIA) department at Ford Motor Company develops scalable solutions to complex real-world problems using Machine Learning, Big Data, Statistics, Econometrics, and Optimization to drive evidence-based decision-making.

We are seeking a hands-on Lead Data Scientist to drive the technical development and successful delivery of AI/ML projects. In this role, you will act as a senior individual contributor and technical project driver, working directly with business partners to translate complex business challenges into robust machine learning solutions. You will be responsible for the end-to-end lifecycle of these products—from initial framing and hands-on model development to deployment and business integration.

Key Roles and Responsibilities of Position:

• Lead the technical delivery of AI/ML projects, ensuring that products are developed, validated, and successfully integrated to solve critical business problems.
• Partner closely with business stakeholders to understand requirements, translate business problems into analytically tractable frameworks, and deliver actionable, data-driven insights.
• Design and implement data analysis and ML models, hypotheses, algorithms and experiments to support data-driven decision-making.
• Mentor junior data scientists and guide them in solving analytics related problems
• Apply various analytics techniques like data mining, predictive modeling, prescriptive modeling, math, statistics, advanced analytics, machine learning models and algorithms, etc.; to analyze data and uncover meaningful patterns, relationships, and trends
• Design efficient data loading, data augmentation and data analysis techniques to enhance the accuracy and robustness of data science and machine learning models, including scalable models suitable for automation
• Research, study and stay updated in the domain of data science, machine learning, analytics tools and techniques etc.; and continuously identify avenues for enhancing analysis efficiency, accuracy and robustness

Key Roles and Responsibilities of Position:

• Lead the technical delivery of AI/ML projects, ensuring that products are developed, validated, and successfully integrated to solve critical business problems.
• Partner closely with business stakeholders to understand requirements, translate business problems into analytically tractable frameworks, and deliver actionable, data-driven insights.
• Design and implement data analysis and ML models, hypotheses, algorithms and experiments to support data-driven decision-making.
• Mentor junior data scientists and guide them in solving analytics related problems
• Apply various analytics techniques like data mining, predictive modeling, prescriptive modeling, math, statistics, advanced analytics, machine learning models and algorithms, etc.; to analyze data and uncover meaningful patterns, relationships, and trends
• Design efficient data loading, data augmentation and data analysis techniques to enhance the accuracy and robustness of data science and machine learning models, including scalable models suitable for automation
• Research, study and stay updated in the domain of data science, machine learning, analytics tools and techniques etc.; and continuously identify avenues for enhancing analysis efficiency, accuracy and robustness

Minimum Qualifications

• Bachelor’s degree in Data Science, Computer Science, Operational Research, Statistics, Applied Mathematics, or a related engineering discipline.

• Demonstrated experience of translating a business problem into an analytically tractable framework, leveraging analytics to solve the problem, generating insights from the solution, and working with the business to use the insights to deliver business value

• 6+ years of hands-on experience in Python programming for data analysis and machine learning / generative AI.

• 6+ years of hands-on experience with data modeling, data mining, machine learning algorithms or statistical analysis.

• 2+ years' experience in SQL programming language and relational databases.

Preferred Qualifications

• An MS/PhD in Computer Science, Operational research, Statistics, Applied mathematics, or in any other engineering discipline. PhD strongly preferred.

• Experience working with Google Cloud Platform (GCP) services, leveraging its capabilities for ML model development and deployment.
• Experience with Git and GitHub for version control and collaboration.
• Besides Python, familiarity with one more additional programming language (e.g., C/C++/Java)
• Strong background and understanding of mathematical concepts relating to probabilistic models, conditional probability, numerical methods, linear algebra, neural network under the hood detail.
• Experience working with large language models such GPT-4, Google, Palm, Llama-2, etc.
• Excellent problem solving, communication, and data presentation skills.