Moody’s Recruitment | Intern | Fresher | Apply Now

  • Full Time
  • Noida, India
  • INR 5 LPA (via Glassdoor) INR / Year

Moody’s

Company Name: Moody’s
Post Name: Intern
Salary: INR 5 LPA (via Glassdoor)
Degree: Bachelor’s/Master’s Degree
Batch: 2019/2020/2021/2022/2023/2024
Experience: Fresher(0-1yr)
Job Location: Noida, India

Job Description & Responsibilities:-

  • Draw meaningful insights and inferences based on various white and research papers on different insurance domains, available online.
  • Enhancing data collection procedures to include information that is relevant for building analytic systems.
  • Should be able to automate various processes on regular basis using Python/R etc.
  • Provide professional skills necessary for all phases of data analysis, including the application of standard statistical methods for conducting analysis, QA/QC/, documentation and presentation.
  • Communicates analytical insights through sophisticated synthesis and packaging of results (Including PPT slides and charts)
  • Serve as an active participant on cross-functional projects, interpreting data, and translating into actionable insights, provide support on ad-hoc analysis and reports.

Eligibility Criteria:-

  • BS / MS degree in computer science or science related field including but not limited to
  • Operations Research, Applied Mathematics, Econometrics and Statistics
  • 0-1 years of hands-on experience in similar roles
  • Proficient in Python/R programming skills. Julia programming skill is a plus
  • Data manipulation tools (SQL, Microsoft Office); high level of expertise in Excel.
  • Excellent written/documentation and verbal skills, as evidenced by white papers or technical presentations at meetings and conferences.
  • Analytical and innovative thinking and problem-solving skills.
  • Good, applied statistics skills, such as distributions, statistical testing, regression, etc.

Preferred Skill Set:-

  • Experience in one or more of the following would be useful: data mining, text mining, machine learning developing statistical models.
  • Detail-oriented, self-driven quick learner with very strong analytical and problem-solving skills, and a high degree of self-motivation
  • Ability to independently drive projects and demonstrate technical ownership of the work
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