דרושים מדען נתונים ב-Ford ב-India, Chennai

Data Engineer - GSR5/GSR6

This role requires a combination of software engineer, data enginner and an ML developer where you're expected to build solutions integrating data pipelines & ML models. It also requires you to have basic knowledge and (preferably) hands-on experience on building back-end services(APIs) which integrate data & applications.

• As a member of the Credit Analytics team in GDIA, the candidate will perform implementation, testing and deployment of predictive models and strategies.
• As a member of the Solutions deployment team, candidate's main responsibilities will be to:
• Analyze, Implement, test and deployment of predictive models and risk strategies globally using multiple platforms
• Collaborate with business partners, Analytics, IT teams and Vendors to production Alize predictive models and Strategies
• Generate viable ideas and develop POCs for testing efficiencies
• Upgrade of deployment platforms
• Provide ad hoc reporting, data analysis and programming as requested by Business customers
• Proactively work to continuously improve the business analytics delivery process
• Play roles of data and technology consultant
• Working with International teams an added plus

You will work with diverse datasets, from structured logs to unstructured events, to build intelligent systems for event correlation, root cause analysis, predictive maintenance, and autonomous remediation, ultimately driving significant operational efficiencies and improving service availability. This position requires a blend of deep technical expertise in machine learning, a strong understanding of IT operations, and a commitment to operationalizing AI solutions at scale.

1. Enable data integration/ETL delivery by providing consulting services to all development teams:

• Problem solving for production and critical launches
• Tool selection
• Design and develop solution
• Hiring BI talents
• Performance tuning
• How to guidance

2. Drive enterprise technology adoption & onboard PDO teams to IICS or MDM
3. Conduct PoCs for new capabilities and integration
4. Modernize technology / Explore emerging technologies

​ ​

Role & Responsibilities

• Utilize Google Cloud Platform & Data Services to modernize legacy applications.
• Understand technical business requirements and define architecture solutions that align to Ford Motor & Credit Companies Patterns and Standards.
• Collaborate and work with global architecture teams to define analytics cloud platform strategy and build Cloud analytics solutions within enterprise data factory.
• Provide Architecture leadership in design & delivery of new Unified data platform on GCP.
• Understand complex data structures in analytics space as well as interfacing application systems. Develop and maintain conceptual, logical & physical data models. Design and guide Product teams on Subject Areas and Data Marts to deliver integrated data solutions.
• Leverage cloud AI/ML Platforms to deliver business and technical requirements.
• Provide architectural guidance for optimal solutions considering regional Regulatory needs.
• Provide architecture assessments on technical solutions and make recommendations that meet business needs and align with architectural governance and standard.
• Guide teams through the enterprise architecture processes and advise teams on cloud-based design, development, and data mesh architecture.
• Provide advisory and technical consulting across all initiatives including PoCs, product evaluations and recommendations, security, architecture assessments, integration considerations, etc.

We are seeking a highly motivated and experienced Data Product Manager to lead the strategy, roadmap, and execution of our data ingestion platform. This platform is critical to our organization, ingesting a wide variety of data types—structured, unstructured, real-time, and batch—from diverse sources. The ideal candidate is a seasoned product leader with a proven track record of successfully delivering data products that meet and exceed user needs. This role requires a strong understanding of data engineering principles, product management best practices, and a passion for building high-quality, scalable, and reliable data solutions.