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Assistant Director - Data Scientist

Pay
$132,600–192,300/year · BaseAnnual period assumed. Location-specific pay — pay source
The 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.
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Work setting
Not stated
Employment
Not stated

What you’ll work on

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

From the employer’s posting
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

What you’ll bring

All qualifications

Core experience

  • 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]
Qualification wording
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 & alternatives
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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Status in our records
Live
First seen by us
Oct 6, 202639 days ago
Recorded sightings
153
Last seen by us
Nov 13, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

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.

Time since we first saw it

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.

Moody's is hiring

54 open jobs

Open jobs by the week we first saw them, last 12 weeks.

  • Takes jobs down at a usual pace
  • 11% of open jobs were reposted
Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

  • Python
  • R
  • SQL
  • AWS
  • Azure
  • GCP
Where the posting names them
- 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.)

Your next step

Complete your application on careers.moodys.com. The employer’s form will show what is required.

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About Moody's

People
About 5,300 in 2025, up 10% from the year before.Annual report (SEC Form 10-K)
Company
Listed as MCO.

Demand for Data Scientist roles

New Data Scientist postings at the employers we follow are down 4% over the last 6 weeks compared with the 6 before.

Weekly new Data Scientist postings at the same employers, from the week of August 17 to the week of November 2. The fewest in a week was 1,403, the most 2,949, and the latest week had 2,098.
August 17Last week

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.

About the role

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.

US government figures for data scientists

Median yearly pay, May 2025
$120,230
Expected change, 2025 to 2035
34.6% more jobsagainst 3.5% for all jobs

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.

Job description

View original posting ↗

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. 


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
  • 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
  • Adaptable and curious: comfortable moving between projects and subject areas and learning new domains quickly
  • Demonstrated proficiency in artificial intelligence concepts, with hands-on experience using AI tools to streamline workflows and enhance operational efficiency. Proven ability to implement AI-powered solutions to solve business challenges. Demonstrates a growing awareness of AI risk management and a commitment to responsible and ethical AI use
  • Experience in credit risk modeling (e.g., default prediction, early warning, or scoring models) or other financial modeling
  • Knowledge of accounting and financial statement analysis
  • Experience with large-scale data tools and formats such as Spark, Athena, or Parquet, and general database concepts
  • 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.)
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]
  • CPA [Preferred]
Responsibilities
Drive quantitative modeling innovation at Moody's Credit Center of Excellence to enhance credit analytics and predictive modeling capabilities globally.
  • Partner across Moody's business lines to enhance modeling and analytical frameworks, incorporating state-of-the-art techniques
  • Design and deliver innovative analytical solutions, leveraging quantitative methods to address complex financial, economic, and operational problems
  • Build modeling datasets from large, varied sources of financial, market, and firm-level data; assess data quality, handle missing or inconsistent data, and construct sound estimation samples
  • 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
  • Communicate technical subject matter clearly and concisely, ensuring that insights, limitations, and implications are well understood by diverse audiences
  • Contribute to projects across the Credit Center of Excellence as priorities evolve, quickly learning existing models, codebases, and subject areas
About the Team
The 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.

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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Location & working pattern

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
More source context
- 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
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