Pay
In line with similar jobs
$133K–$192K against $118K–$195K, the middle half of what similar US postings state.
From 3,828 postings in the last year. Stated ranges, not salaries paid.
New York, NY
Live, first seen by us 39 days agoThe Credit Center of Excellence (COE) at Moody's is dedicated to developing, enhancing and maintaining our industry-leading credit analytics and predictive modelling capabilities. Our analytics and models are used by institutions worldwide to make credit, risk management, pricing, and investment decisions. We are a global team (spread across all US time zones, GMT, GMT+1) and we work closely with various teams including product management, commercial strategy, and go-to-market leaders to ensure the delivery of high-quality credit risk assessments and solutions. This collaborative approach allows the COE to integrate seamlessly into Moody's structure to support and grow our customers' business operations and enhance their ability to navigate risk. For US-based roles only: the anticipated hiring base salary range for this position is $132,600.00 - $192,300.00, depending on factors such as experience, education, level, skills, and location. This range is based on a full-time position. In addition to base salary, this role may be eligible for a completion bonus. Moody’s also offers insurance and a discounted employee stock purchase plan for limited duration employees. Moody’s is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, sex, gender, age, religion or creed, national origin, ancestry, citizenship, marital or familial status, sexual orientation, gender identity, gender expression, genetic information, physical or mental disability, military or veteran status, or any other characteristic protected by law. Moody’s also provides reasonable accommodation to qualified individuals with disabilities or based on a sincerely held religious belief in accordance with applicable laws. If you need to inquire about a reasonable accommodation, or need assistance with completing the application process, please email accommodations@moodys.com. This contact information is for accommodation requests only, and cannot be used to inquire about the status of applications.Read the full posting
Build modeling datasets from large, varied financial, market and firm-level sources
Re-estimate, recalibrate and enhance established credit models
Decide when traditional, machine-learning or hybrid methods fit a problem
Write model documentation and methodology materials for governance and clients
Design analytical solutions for financial, economic and operational problems
Build modeling datasets from large, varied sources of financial, market, and firm-level data
Contribute to the ongoing development of established models, including re-estimation, recalibration, performance analysis, and methodology enhancements
Evaluate when traditional, machine-learning, or hybrid approaches best fit a problem
Write clear model documentation and methodology materials that support internal model governance and client transparency
Design and deliver innovative analytical solutions, leveraging quantitative methods to address complex financial, economic, and operational problems
Master's degree in Financial Engineering, Data Science, Statistics, Economics, Econometrics, Mathematics, Physics, Finance, Accounting, or a related quantitative field with 2 years of industry experience in quantitative modeling [Required]
Education - Master's degree in Financial Engineering, Data Science, Statistics, Economics, Econometrics, Mathematics, Physics, Finance, Accounting, or a related quantitative field with 2 years of industry experience in quantitative modeling [Required] - Ph.D. in Financial Engineering, Data Science, Statistics, Economics, Econometrics, Mathematics, Physics, Finance, Accounting, or a related quantitative field [Preferred]
- Master's degree in Financial Engineering, Data Science, Statistics, Economics, Econometrics, Mathematics, Physics, Finance, Accounting, or a related quantitative field with 2 years of industry experience in quantitative modeling [Required] - Ph.D. in Financial Engineering, Data Science, Statistics, Economics, Econometrics, Mathematics, Physics, Finance, Accounting, or a related quantitative field [Preferred] - CPA [Preferred]
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In line with similar jobs
$133K–$192K against $118K–$195K, the middle half of what similar US postings state.
From 3,828 postings in the last year. Stated ranges, not salaries paid.
Day 39
Similar jobs usually come down between day 8 and day 60.
Well past Moody's’s usual: its jobs come down by about day 15.
From 11,783 similar postings that came down, and 78 of Moody's’s. Coming down is not always a hire.
54 open jobs
Open jobs by the week we first saw them, last 12 weeks.
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- Excellent written and verbal communication skills in English, with demonstrated ability to distill complex modeling concepts into clear, audience-appropriate messages for senior leaders, cross-functional partners, and non-technical stakeholders - Strong programming skills in Python or R, plus working knowledge of SQL - Fluency with AI productivity tools including coding agents
- Experience with Git/GitHub in a collaborative development setting - Exposure to cloud platforms such as AWS, GCP, or Azure - Familiarity with model documentation and model risk governance standards (e.g., SR 11-7, SR 26-2, etc.)
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New Data Scientist postings at the employers we follow are down 4% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under Data Scientist, by the work the posting describes.
Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.
Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.
Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.
At Moody's, we unite the brightest minds to turn today’s risks into tomorrow’s opportunities. We do this by striving to create an inclusive environment where everyone feels welcome to be who they are—with the freedom to exchange ideas, think innovatively, and listen to each other and customers in meaningful ways. Moody’s is transforming how the world sees risk. As a global leader in ratings and integrated risk assessment, we’re advancing AI to move from insight to action—enabling intelligence that not only understands complexity but responds to it. We decode risk to unlock opportunity, helping our clients navigate uncertainty with clarity, speed, and confidence.
If you are excited about this opportunity but do not meet every single requirement, please apply! You still may be a great fit for this role or other open roles. We are seeking candidates who model our values: invest in every relationship, lead with curiosity, champion diverse perspectives, turn inputs into actions, and uphold trust through integrity.
For US-based roles only: the anticipated hiring base salary range for this position is $132,600.00 - $192,300.00, depending on factors such as experience, education, level, skills, and location. This range is based on a full-time position. In addition to base salary, this role may be eligible for a completion bonus. Moody’s also offers insurance and a discounted employee stock purchase plan for limited duration employees.
Moody’s is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, sex, gender, age, religion or creed, national origin, ancestry, citizenship, marital or familial status, sexual orientation, gender identity, gender expression, genetic information, physical or mental disability, military or veteran status, or any other characteristic protected by law. Moody’s also provides reasonable accommodation to qualified individuals with disabilities or based on a sincerely held religious belief in accordance with applicable laws. If you need to inquire about a reasonable accommodation, or need assistance with completing the application process, please email accommodations@moodys.com. This contact information is for accommodation requests only, and cannot be used to inquire about the status of applications.
For San Francisco positions, qualified applicants with criminal histories will be considered for employment consistent with the requirements of the San Francisco Fair Chance Ordinance.
This position may be considered a promotional opportunity, pursuant to the Colorado Equal Pay for Equal Work Act.
Click here to view our full EEO policy statement. Click here for more information on your EEO rights under the law. Click here to view our Pay Transparency Nondiscrimination statement.
Candidates for Moody's Corporation may be asked to disclose securities holdings pursuant to Moody’s Policy for Securities Trading and the requirements of the position. Employment is contingent upon compliance with the Policy, including remediation of positions in those holdings as necessary.
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New York, NY
Skills and Competencies - Strong theoretical grounding in econometrics, statistics, machine learning, and other quantitative modeling approaches, with demonstrated ability to determine when traditional, machine-learning, or hybrid methods are most appropriate, based on business context, data availability, and interpretability needs - Experience working with large, real-world datasets: cleaning, merging, exploring, and diagnosing data problems
- Contribute to the ongoing development of established models, including re-estimation, recalibration, performance analysis, and methodology enhancements - Evaluate when traditional, machine-learning, or hybrid approaches best fit a problem, weighing predictive performance against interpretability, stability, and data limitations - Write clear model documentation and methodology materials that support internal model governance and client transparency
This passage needs a closer read in the full description.