CRISIL is an agile and innovative, global analytics company driven by its mission of making markets function better. We are India’s foremost provider of ratings, data, research, analytics, and solutions. A strong track record of growth, a culture of innovation, and a global footprint set us apart. We have delivered independent opinions, actionable insights, and efficient solutions to over 100,000 customers. CRISIL’s businesses operate from India, the US, the UK, Argentina, Poland, China, Hong Kong, and Singapore.
CRISIL is majority-owned by S&P Global Inc., a leading provider of transparent and independent ratings, benchmarks, analytics, and data to the capital and commodity markets worldwide.

Who we serve
CRISIL’s clients range from micro, small and medium companies to large corporates, investors, to top global financial institutions. We work with commercial and investment banks, insurance companies, private equity players, and asset management companies globally.
We also work with governments and policymakers in the infrastructure space in India and in other emerging markets.

About the job

We are looking for interns who are good at Statistics preferably from B.Tech or B.Sc. (Stats) who are ready to do 6 months internship.

JD: Intern (Statistics)

  • · Analytic Project Execution – Own and deliver multiple and complex analytic projects. This would require an understanding of business context and conversion of business problems in modeling.
  • · Always up to date with the latest use cases of modeling community, machine learning, and deep learning algorithms and share knowledge within the team.
  • ·Statistical mindset – Proficiency in basic statistics, hypothesis testing, segmentation and predictive modeling.
  • ·Communication skills – Ability to translate and articulate technical thoughts and ideas

Basic Qualifications

  • ·Proficiency across various data languages – Python, R, and excel macros along with working knowledge of SQL.
  • · Knowledge of working with large and multiple datasets, data warehouses, and the ability to pull data using relevant programs and coding.
  • · Well versed with necessary data preprocessing and feature engineering skills.
  • · Good knowledge of implementing Machine learning algorithms such as Random Forest and Gradient Boosting in solving business problems such as default classification, macro forecasting.
  • · Basic knowledge of implementing deep learning techniques like ANN.
  • · Exposure to deep learning packages like Tensorflow.
  • · Strong background in Statistical Analysis.

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