Data Science Strategist

1 day ago

Dombivli, Maharashtra, India beBeeData Full time ₹ 2,50,00,000 - ₹ 3,00,00,000

Unlock business potential by harnessing the power of data and analytics. At Sigmoid, we are transforming the way organizations make decisions by building modern data architectures in cloud and open-source environments.

Our collaborative approach with the world's largest data producers enables us to tackle complex business problems head-on. We bring expertise in data engineering, predictive analytics, artificial intelligence, and DataOps to drive innovation.

As a recognized leader in North America, Sigmoid is renowned for its excellence in data science. This role is part of our Bengaluru office, offering an opportunity to combine technical expertise and creativity to solve intricate business problems and develop straightforward data solutions.

  • Develop strategic data science roadmaps and lead high-performing teams to execute them effectively.
  • Handle end-to-end AI & analytics programs in a dynamic environment, balancing hands-on contribution, team management, and client interaction.
  • Discover hidden insights in large datasets and drive business results with data-based solutions.
  • Contribute to internal product development initiatives related to data science.
  • Deliver complex projects through excellent project management and ensure timely delivery.
  • Build scalable engagement processes and define technology/strategy roadmaps for clients.
  • Manage team members to adhere to project plans and build trusted advisor relationships with clients and internal leadership.

The ideal candidate will have a strong grasp of machine learning, deep learning, data mining, and statistical concepts, with experience in developing models and solutions in these areas.

Mandatory skills include a B-Tech/M-Tech/MBA from a top-tier Institute, preferably in a quantitative subject, and 14+ years of hands-on experience in applied Machine Learning, AI, and analytics.

Desirable skills include a deep understanding of ML algorithms for common use cases in both structured and unstructured data ecosystems, comfort with large-scale data processing, and ability to work well with minimal guidance.

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