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Senior Data Scientist

airtel

Location

Gurugram, Haryana, India

Salary

Not specified

Type

fulltime

Posted

Today

via linkedin

Job Description

Airtel is hiring for a Data Science professional who is passionate to work in financial services (credit risk) business.

Ever dreamed of working with 2-5 trillion records / day on a 200\+ petabyte base? Want to improve experience of \~400 million consumers using machine learning? Do you want to work with one of world’s largest telco and India’s top brand?

If so, we would like you to join us on an exciting journey to create significant impact using data. The mission of the Data Science team is to transform terabytes of data into robust models for personalization and discovery.

Your work will directly impact the way millions of Indians use our Digital offerings. We would like you to join us on an exciting journey to create advanced ML products.

Responsibilities

  1. Work on complex business problems related to underwriting, risk and cross-sell.
  2. End to end development and testing of the solutions as per the requirements.
  3. Design, run and analyze A/B and multivariate hypothesis tests aimed at optimizing customer experience, business KPIs and product funnels.
  4. Be involved in technology research, capability building across newer technologies and tools in Machine Learning ecosystem at scale.

Qualifications –

As a successful applicant, you should have:

  1. B.Tech or MS in Computer Science / Statistics / Applied Mathematics.
  2. Deep understanding of Statistical Analysis, Modeling and Machine Learning algorithms.
  3. 3 to 6 years of experience with machine learning model development in credit risk and underwriting. Experience in designing and deploying ML modeling and prediction pipelines.
  4. Good command over Python, Tensorflow/PyTorch, PySpark.
  5. Strong coding skills with the ability to write high-performance code in Python/Julia/C\+\+.
  6. Must be fluent with SQL and Hive.
  7. Ability to contribute to multiple projects simultaneously and work in a fast-paced, collaborative and iterative environment.

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