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Moody's

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4.0

based on 171 Reviews

Video summary

55 Moody's Jobs

Analytics & Modeling Analyst

Moodys Investors Service

4.0

based on 171 Reviews

1-3 years

Bangalore / Bengaluru

1 vacancy

Analytics & Modeling Analyst

Moody's

posted 28d ago

Job Role Insights

Flexible timing

Key skills for the job

Python SQL Machine Learning Analytical Chemistry HTML and CSS Corporate Finance

+ 1 more

Job Description

Job Category:

Engineering & Technology

Experience Level: Experienced Hire

Qualifications:

masters degree or Ph.D. in data science, computer science, statistics, mathematics, or a related quantitative field.
1-3 years of experience in machine learning, with a strong knowledge of algorithms and principles.
Proven track record of successfully modeling, building, and putting in production machine learning applications.
Good understanding of the tools explaining machine learning predictions.
Expertise in Python and SQL.
Knowledge of Git and collaboration principles.
Experience in natural language processing is a plus.
Good communication and presentation skills, with the ability to explain complex analytical concepts to people from other fields.
Previous experience in corporate finance or debt markets is preferred.
Knowledge of cloud services, with experience in AWS or Microsoft Azure being a plus.

Role and Responsibilities: This Analytics & Modeling Analyst role is part of the Model Verification team within Credit Strategy & Standards, who will be responsible for verifying the implementation and quality assurance of Moodys proprietary predictive analytics models. The successful candidate will ensure that models are accurately implemented, perform as expected, and meet the highest standards of quality.

Key responsibilities include:

Verifying the implementation of predictive analytics models against requirements and specification to ensure they are correctly coded and function as intended.
Conducting quality assurance tests to validate model performance and accuracy.
Collaborating with data scientists, technology teams, and other stakeholders to ensure models are robust and reliable.
Developing and maintaining documentation for model verification processes and results.
Communicating findings and recommendations to business stakeholders and decision-makers.
Staying current with the latest research and technology developments in predictive analytics and model verification.
Evaluating and selecting the best tools and IT architectures for model verification.