Pay
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
Machine Learning Modeling Lead - Credit Modeling
San Francisco, California, United States
Live, first seen by us 9 days ago- Pay
- Pay not listedSimilar roles state $166K–$240K
- Work setting
Hybrid — work setting source
Work setup in the employer’s posting: Hybrid
From the employer’s posting- Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting
What you’ll work on
Full postingLead ML solutions from data exploration and feature engineering to deployment and monitoring
Review junior scientists' submissions and suggest improvements
Manage model documentation, model reviews, and queries from validation teams
Work with implementation teams to deploy models to cloud or on-premises production
Translate banking domain challenges into data-driven solutions with business stakeholders
From the employer’s posting
Lead end-to-end ML solution development & innovation from data exploration, feature engineering, model development, validation, deployment, and monitoring.
Support and review junior scientist submissions and share enhancement suggestions
Responsible for managing queries raised by Validation teams for the model
Collaborate with implementation teams to deploy models into production environments (cloud or on premises).
Work closely with business stakeholders to translate banking domain challenges into data-driven solutions.
What you’ll bring
All qualificationsCore experience
- Knowledge of MLOps frameworks (MLflow, Kubeflow, Airflow, Docker, Kubernetes) is added benefit.
- Excellent communication skills and ability to explain complex concepts to non-technical stakeholders.
Qualification wording
Knowledge of MLOps frameworks (MLflow, Kubeflow, Airflow, Docker, Kubernetes) is added benefit.
Excellent communication skills and ability to explain complex concepts to non-technical stakeholders.
Education & alternatives
- Demonstrable leadership ability, superior problem solving and people management skills - Master’s or Similar in Computer Science, Data Science, Statistics, Applied Mathematics, or a related quantitative field Skills:
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Oct 27, 20269 days ago
- Recorded sightings
- 20
- Last seen by us
- Nov 5, 2026
- Reposted
- once since Aug 14, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications. We count a repost when the same title and location comes back as a new listing within 60 days of the last one coming down. Some hiring systems expire listings on a schedule, so repost rates vary by system.
How this job compares
Time since we first saw it
Day 9
Similar jobs usually come down between day 6 and day 70.
At EXL Service, jobs usually come down by about day 17.
From 4,504 similar postings that came down, and 220 of EXL Service’s. Coming down is not always a hire.
EXL Service is hiring
203 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- Takes jobs down at a usual pace
- 4% of open jobs were reposted
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
- SQL
- Docker
- Kubernetes
- MLflow
- Airflow
- NumPy
- pandas
- PyTorch
- scikit-learn
- TensorFlow
- XGBoost
Where the posting names them
Skills: - Expert in Python, SQL, ML libraries (Numpy, Pandas, Scikit-learn, TensorFlow, PyTorch) and techniques (Regression, Decision Trees, Ensembles: XGBoost, GBM, Random Forest, Unsupervised Learning, etc.). - Knowledge of MLOps frameworks (MLflow, Kubeflow, Airflow, Docker, Kubernetes) is added benefit.
- Expert in Python, SQL, ML libraries (Numpy, Pandas, Scikit-learn, TensorFlow, PyTorch) and techniques (Regression, Decision Trees, Ensembles: XGBoost, GBM, Random Forest, Unsupervised Learning, etc.). - Knowledge of MLOps frameworks (MLflow, Kubeflow, Airflow, Docker, Kubernetes) is added benefit. - Strong grasp of statistical modeling, optimization, and deep learning techniques.
Your next step
Complete your application on fa-ewjt-saasfaprod1.fa.ocs.oraclecloud.com. The employer’s form will show what is required.
Applying through Oracle Recruiting
- Applying starts by asking for your email address.
- Each employer sets which files it accepts, so read the note beside the upload field.
Already applied? Track this application
About EXL Service
- People
- About 65,000 in 2025, up 9% from the year before.Annual report (SEC Form 10-K)
- Company
- Listed as EXLS.
Demand for ML Engineer roles
New ML Engineer postings at the employers we follow are up 12% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 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 ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Job description
We are seeking a highly skilled and experienced Machine Learning Modeling Lead - Credit Modeling to join our ML Model Innovation team within the banking/fintech domain (digital lending). The ideal candidate will be responsible for leading the development and deployment of machine learning models that power key business decisions such as collections models, credit risk scoring, fraud detection, customer segmentation, and personalized financial services.
The Individual needs to have strong knowledge of banking business, data and domain across the customer lifecycle as well as bureau data. They will collaborate with cross-functional teams and provide technical leadership to junior ML modelers and data scientists.
Key Roles and Responsibilities -
- Lead end-to-end ML solution development & innovation from data exploration, feature engineering, model development, validation, deployment, and monitoring.
- Develop robust models which can drive business benefits. Support and review junior scientist submissions and share enhancement suggestions
- Responsible for documentation/documentation reviews, model reviews and submission
- Responsible for managing queries raised by Validation teams for the model
- Collaborate with implementation teams to deploy models into production environments (cloud or on premises).
- Work closely with business stakeholders to translate banking domain challenges into data-driven solutions.
- Guide junior data scientists and engineers on best practices in model development
- Continuously evaluate new tools, technologies, and frameworks relevant to ML in finance.
- Publish internal research and promote a culture of innovation and experimentation.
Candidate Profile:
- Strong business knowledge of banking analytics across the retail banking customer lifecycle.
- 9+ years of experience in applied machine learning model development in the banking or financial services domain.
- Hands-on experience leading ML projects and teams.
- Strong experience with model development, deployment and monitoring in production environments.
- Familiarity with collections, underwriting, fraud and ethical considerations in banking ML models.
- Demonstrable leadership ability, superior problem solving and people management skills
- Master’s or Similar in Computer Science, Data Science, Statistics, Applied Mathematics, or a related quantitative field
Skills:
- Expert in Python, SQL, ML libraries (Numpy, Pandas, Scikit-learn, TensorFlow, PyTorch) and techniques (Regression, Decision Trees, Ensembles: XGBoost, GBM, Random Forest, Unsupervised Learning, etc.).
- Knowledge of MLOps frameworks (MLflow, Kubeflow, Airflow, Docker, Kubernetes) is added benefit.
- Strong grasp of statistical modeling, optimization, and deep learning techniques.
- Excellent communication skills and ability to explain complex concepts to non-technical stakeholders.
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
San Francisco, California, United States
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.