Data Scientist Professional

You must have deep technical experience working with technologies related to artificial intelligence, machine learning and deep learning. A strong mathematics and statistics background is preferred, in addition to experience building complex classification/extraction models extracting data from documents i.e. form, unstructured documents and IDs. You will be familiar with the ecosystem of consulting partners and software vendors in the AI/ML space, and will leverage this knowledge to help Intellect customers in their selection process.

Technical skills:

• AI architecture and pipeline planning.

  Understand the workflow and pipeline architectures of ML and deep learning workloads. An in-depth knowledge of components and architectural trade-offs involved across the data management, governance, model building, deployment and production workflows of AI is a must.

• Data science and advanced analytics

  , including knowledge of advanced analytics tools (such as Python) along with applied mathematics, ML and Deep Learning frameworks (such as PyTorch, Tensorflow) and ML techniques (such as neural networks, transformers and graphs).

• AWS AI services,

  Handson experience to Build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows

  AWS ML services,

  Handson experience to Build, train, deploy, monitor and govern machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows

• MLOps principles

  . Exposure to AWS MLOps offerings

Non-technical skills include:

• Wealth / Insurance Domain Knowledge

  is advantageous.

• Thought leadership.

  Be change agents to help the organization adopt an AI-driven mindset. Take a pragmatic approach to the limitations and risks of AI, and project a realistic picture in front of executives/product owners who provide overall digital thought leadership.

• Collaborative mindset

  . To ensure that AI platforms deliver both business and technical requirements, seek to collaborate effectively with data scientists, data engineers, data analysts, ML engineers, other architects, business unit leaders and CxOs (technical and nontechnical personnel), and harmonize the relationships among them.