Manager, Data Science, Solution Architect

2 days ago

Mumbai, Maharashtra, India Mondelez Full time US$ 1,50,000 - US$ 2,00,000 per year

Purpose of Role

The Solution Architect will provide end-to-end solution architecture guidance for data science initiatives. A successful candidate will be able to handle multiple projects at a time and drive the right technical architecture decisions for specific business problems. The candidate will also execute PoC/PoVs for emerging AI/ML technologies, support the strategic roadmap and define reusable patterns from which to govern project designs.

Main Responsibilities: -

  • Determine which technical architectures are appropriate for which models and solutions.
  • Recommend what technologies and associated configurations are best to solve business problems.
  • Define and document data science architectural patterns.
  • Ensure project compliance with architecture guidelines and processes.
  • Provide guidance and support to development teams during the implementation process.
  • Develop and implement processes to execute AI/ML workloads.
  • Configure and optimize the AI/ML systems for performance and scalability.
  • Stay up to date on the latest AI/ML features and best practices.
  • Integrating SAP BW with SAP S/4HANA and other data sources.
  • Review and sign off on high-level architecture designs.

Career Experiences Required & Role Implications:-

  • Bachelors degree in computer science or related field of study.
  • 10+ years of experience in a global company in data-related roles (5+ years in data science).
  • Strong proficiency in Databricks and analytical application frameworks (Dash, Shiny, React).
  • Experience with data engineering using common frameworks (Python, Spark, distributed SQL, NoSQL).
  • Experience leading complex solution designs in a multi-cloud environment.
  • Experience with a variety of analytics techniques: statistics, machine learning, optimization, and simulation.
  • Experience with software engineering practices and tools (design, testing, source code management, CI/CD).
  • Deep understanding of algorithms and tools for developing efficient data processing systems.

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