Data Science Professional

2 weeks ago

Bengaluru, Karnataka, India ExxonMobil Full time
About ExxonMobil

We are a global energy company with a long history of innovation and a commitment to delivering energy to meet the world's growing needs. Our vision is to lead in energy innovations that advance modern living and a net-zero future.

We are looking for talented individuals to join our team and help us achieve our vision. As a Machine Learning Engineer, you will be part of a team that is responsible for designing, developing, and deploying data science solutions that are scalable, reproducible, and of commercial-grade quality.

Key Responsibilities
  • Apply software development practices, DevOps skills, and Machine Learning techniques to orchestrate an end-to-end machine learning workflow that effectively brings ML models to production.
  • Participate in the scoping of deployment of new data science solutions and implement the appropriate solution design.
  • Sustain data science solutions by enabling continuous ML model and/or service performance monitoring, training, and re-training of models, including the implementation of proactive alerting methods.
  • Work effectively with computational scientists, data scientists, engineers, software developers, and domain experts across the globe to develop and apply computational and data science solutions in support of our business.
About You

We are looking for individuals with a strong foundation in data science and machine learning. The ideal candidate will have a Bachelor's degree from a recognized university in Computer Science, IT, Applied Mathematics, Engineering, or related disciplines, with a minimum CGPA of 7.0 or equivalent. Additionally, the candidate should have at least 3 years of experience in data science and machine learning or related computational domains.

  • Competent to expert-level programming experience in C/C++/Python.
  • Strong foundation in application design.
  • Experience with refactoring legacy code and leveraging third-party libraries/APIs during software development.
  • Experience with Source code version control (Git), Azure Cloud platform, and containers, Databricks, and MLflow.
  • Continuous Integration and Continuous Deployment.
  • Familiarity with statistical analysis, regression, and classification.

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