Job Description

We are seeking a ML Data Engineer with a wealth of experience creating and taking ownership of data pipelines, to consume and interpret our unique GIS dataset. The analyses you deploy will range from simple stats all the way up to machine learning solutions.

Our unprecedented technology requires highly skilled and talented engineers to join a close-knit team willing to understand and take ownership of the problems they solve and the solutions they deploy. This is a great opportunity to join at the early stage of a rapidly growing company and define your own success story within it.

Your team
Data Engineering at RedOptima accelerates value delivery for internal and external stakeholders by providing production ready solutions & actionable insights. We cater to every aspect of RedOptima’s data, supercharging their success through data driven innovation.

You will be working hands-on with a diverse team, with a variety of skill sets across the fields of Data Analytics, Machine Learning, Computer Vision, Artificial intelligence, and Geospatial analytics. The RedOptima engineering team is currently based out of our Gurgaon office.

Responsibilities and tasks

  • Applying expertise in statistics and ML to tackle real problems using our unprecedented collision risk analytics platform
  • Identifying new opportunities to automate and create insights using our diverse data sources
  • Staying up-to-date in cutting-edge numerical and computer vision methods in ML space
  • Productionising models on AWS infrastructure
  • Ownership from prototype/MVP to automation, deployment, and maintenance
  • Deploying monitoring and DQ automation to health check solutions in production
  • Coaching team members in solution ownership and best practice in development and design
  • Communicating complex concepts to client-facing teams and non-experts
  • Creating and maintaining systems that are optimised, robust and scalable
  • Identify requirements and critical path to solve problems, give clear timeline estimates quantifying any uncertainty
  • Take data-driven approaches to validate assumptions/issues and track progress on changes being implemented