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Ford Motor Company
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Data Science/AI Manager - Design Cost Analytics

Data Science/AI Manager - Design Cost Analytics

Ford Motor Company

5 years

5 - 9 Lacs

chennai

Posted:1 day ago| Platform: GlassDoor logo

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Skills Required

data ai design analytics power drive evidence ml support service marketing strategy finance learning software cutting development leadership analysis simulation econometrics optimization engineering statistics algorithms python tensorflow pytorch xgboost gcp sql spark hive communication collaboration network vision reliability jira architecture certifications agile devops testing code model training deployment strategies coding stability compliance risk assessment consistency research engagement manufacturing management

Work Mode

On-site

Job Type

Part Time

Job Description

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.

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 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.

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 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.

Technical Leadership: Provide expert technical guidance and mentorship to data scientists & software engineers in the product team, actively contribute to code reviews, 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.

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Data Science/AI Manager - Design Cost Analytics