Data Science/AI Manager leading analytical and AI/ML product teams for Ford's data-driven solutions. Mentoring and developing talent while ensuring high-quality technical outcomes.
Responsibilities
Provide expert technical guidance and mentorship to data scientists & software engineers in the product team.
Actively contribute to code reviews, design discussions, algorithm selection, and architecting scalable data science solutions.
Lead, mentor, and develop a high-performing team of highly skilled data scientists, software engineers, and ML engineers.
Contribute to the development and implementation of the team's innovation strategy, focusing on new product development and leveraging emerging technologies.
Lead engagement with internal business partners and efficiently translate business requirements into data & analytical problem formulations.
Clearly and concisely communicate technical information to both technical and non-technical audiences.
Lead the problem formulation, research, development, and implementation of GenAI to enhance existing solutions and create new innovative applications.
Work effectively with various teams and stakeholders to achieve business objectives.
Requirements
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 algorithms, deep learning architectures, and statistical modeling techniques (Automotive 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 cloud-based platforms (e.g., GCP) and data related technologies (e.g., SQL, Spark, Hive).
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.
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 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.
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