Job Location: Bangalore/Bengaluru
- Collaborate closely with internal and external product stakeholders, both technical and non-technical and help translate deep machine learning knowledge to product applications
- Work closely with ML engineering and platform team to help define the vision of Machine Learning training and inference platforms running on the cloud
- Break down larger ML initiatives into smaller problems that enables data science to deliver incremental business value and lead the team to execute on them
- Mentor and grow other data scientists and engineers in the team on both data science modeling and software engineering skills
- Incorporate up-to-date ML technology and DS approach as best practice for the team
- Help in continuing to build out and expand the Data Science and ML Engineering teams
- Work effectively in a dynamic, changing environment while focusing on key goals and objectives
- Advanced degree in Data Science, Statistics, Applied Math, Computer Science, Engineering or
other equivalent quantitative disciplines - 7+ years of industry experience in the field of Data Science and Machine Learning
- Strong proficiency in Python and SQL; experience with some of the following languages, tools,
and frameworks: R, Spark, Scala, scikit-learn, Tensorflow, PyTorch, etc. - Familiarity with standard software engineering practices and tools including object-oriented
programming, test-driven development, CI/CD, git, shell scripting, task orchestration (Airflow,
Luigi, etc.) and preferably AWS tooling (Sagemaker, DynamoDB, ECS, etc.) - Strong knowledge of underlying mathematical foundations of statistics and machine learning
- Prior success deploying machine learning solutions in large-scale production environments
- Deep experience in one or more of the following areas: natural language processing,
recommender systems, and deep learning
- Experience collaborating with cross-functional teams and stakeholders to evaluate new Machine Learning opportunities
- Problem solver who loves to dig into different kinds of data and can communicate their findings to cross-functional stakeholders
- A successful track record of developing financial risk and fraud detection solutions in payment
systems.
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