דרושים אנליסט מידע 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

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

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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.

§ Hands-on Expertise in Python programming and experience in handling various data science libraries (Pandas, Numpy, Scipy) would be a necessary requirement

§ Familiarity with analytics methods (descriptive/predictive/prescriptive) and Business Intelligence tools (Python-Matplotlib, PowerBI, QlikSense) would be a definite plus.

§ Exposure to Cloud technologies (e.g., Google Cloud), including executing Machine Learning algorithms on Cloud would be definite plus.