Pay
$154K–$226K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 3,103 postings in the last year. Stated ranges, not salaries paid.
Reads like a Data Leadership role: running a data team.
New York City Office · New York, United States
Actively hiring, first seen by us 8 days agoLocation type: HybridFrom the employer’s posting
Employment type: Full-timeFrom the employer’s posting
Department: All Departments › DataFrom the employer’s posting
Will you now or in the future require Plaid to provide immigration-related support or sponsorship in order to employ you?From the employer’s application form
As the Manager of Machine Learning Engineering on the Credit team, you’ll lead the team responsible for Plaid’s LendScore and Income Insights products.
Lead, support, and develop a team of Machine Learning Engineers and Data Scientists working across credit insights products.
Lead, support, and develop a team of ML engineers and data scientists
Set technical and product direction and the ML roadmap for LendScore and Income Insights
Guide ML models from experimentation and offline training to online serving and monitoring
Prioritize work with Engineering, Product, and Data teams for reliable, scalable solutions
Balance new 0-to-1 opportunities with scaling existing products
As the Manager of Machine Learning Engineering on the Credit team, you’ll lead the team responsible for Plaid’s LendScore and Income Insights products. You’ll manage and develop a team of Machine Learning Engineers and Data Scientists, set direction for advancing our insights, and partner with Engineering, Product, and other Data teams to deliver customer and business impact.
Lead, support, and develop a team of Machine Learning Engineers and Data Scientists working across credit insights products.
Set technical and product direction for evolving LendScore and Income Insights, including the team’s machine learning roadmap.
Guide the development of machine learning models across the full lifecycle, from experimentation and offline training to online serving and monitoring.
Partner with Engineering, Product, and Data teams to prioritize opportunities and deliver reliable, scalable solutions.
Help the team tackle both 0-to-1 opportunities and the scaling and improvement of established products.
- Ability to work effectively with both technical and non-technical teams. - Master’s degree or equivalent work experience in Computer Science, Mathematics, Engineering, or a closely related field. - Nice to have: Experience in data analytics or data engineering.
Actively hiringFirst seen by us 8 days ago, still listed, and not a repost of an earlier posting.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
$154K–$226K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 3,103 postings in the last year. Stated ranges, not salaries paid.
Day 8
Similar jobs usually come down between day 8 and day 57.
At Plaid, jobs usually come down by about day 16.
From 9,498 similar postings that came down, and 19 of Plaid’s. Coming down is not always a hire.
14 open jobs
Open jobs by the week we first saw them, last 12 weeks.
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.
- Experience building or working with data-intensive backend applications in large, distributed systems. - Ability to code and iterate independently using data infrastructure tools such as Python, Spark, Jupyter notebooks, and standard machine learning libraries. - A track record of taking ownership and driving projects to business impact.
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New Data Leadership postings at the employers we follow are about the same 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 Leadership, by the work the posting describes.
Data leaders run data teams or design how a company's data fits together. Managers hire, set priorities and answer for whether the data work pays off; architects choose the systems and the shape of the data.
Expect years of hands-on data work, experience leading people or projects, and the ability to explain tradeoffs to executives.
Unlike a senior engineer or analyst, most of a data leader's week goes to people, planning and decisions rather than building.
The government has no code for data leaders, so these figures are for the closest group, computer and information systems managers, which includes data processing and data operations managers.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 11-3021.
We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam.
As the Manager of Machine Learning Engineering on the Credit team, you’ll lead the team responsible for Plaid’s LendScore and Income Insights products. You’ll manage and develop a team of Machine Learning Engineers and Data Scientists, set direction for advancing our insights, and partner with Engineering, Product, and other Data teams to deliver customer and business impact.
In this role, you will:
Lead, support, and develop a team of Machine Learning Engineers and Data Scientists working across credit insights products.
Set technical and product direction for evolving LendScore and Income Insights, including the team’s machine learning roadmap.
Partner with Engineering, Product, and Data teams to prioritize opportunities and deliver reliable, scalable solutions.
Guide the development of machine learning models across the full lifecycle, from experimentation and offline training to online serving and monitoring.
Help the team tackle both 0-to-1 opportunities and the scaling and improvement of established products.
Foster a high-ownership, bottom-up team culture grounded in data-driven decisions and business impact.
Leading work that can expand access to financial services for millions of people through well-known and emerging fintech applications.
Developing products and insights that support the next wave of cash-flow-based underwriting.
Helping a talented team experiment with modern machine learning techniques and bring them into production.
Shaping a roadmap that spans new 0-to-1 opportunities and scaling solutions that are already delivering value.
Working across Data, Engineering, and Product in a high-ownership environment where the team’s work can drive meaningful impact.
8+ years of experience training and serving AI or machine learning models in production environments.
Experience leading a team of Data Scientists and Machine Learning Engineers.
Experience in fintech lending and a strong understanding of how models are built and used in lending.
Experience building or working with data-intensive backend applications in large, distributed systems.
Ability to code and iterate independently using data infrastructure tools such as Python, Spark, Jupyter notebooks, and standard machine learning libraries.
A track record of taking ownership and driving projects to business impact.
Ability to work effectively with both technical and non-technical teams.
Master’s degree or equivalent work experience in Computer Science, Mathematics, Engineering, or a closely related field.
Nice to have: Experience in data analytics or data engineering.
Our mission at Plaid is to unlock financial freedom for everyone. To support that mission, we seek to build a diverse team of driven individuals who care deeply about making the financial ecosystem more equitable. We recognize that strong qualifications can come from both prior work experiences and lived experiences. We encourage you to apply to a role even if your experience doesn't fully match the job description. We are always looking for team members that will bring something unique to Plaid!
Plaid is proud to be an equal opportunity employer and values diversity at our company. We do not discriminate based on race, color, national origin, ethnicity, religion or religious belief, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, military or veteran status, disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state, and local laws. Plaid is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance with your application or interviews due to a disability, please let us know at accommodations@plaid.com.
Please review our Candidate Privacy Notice here.
Additional compensation in the form(s) of equity and/or commission are dependent on the position offered. Plaid provides a comprehensive benefit plan, including medical, dental, vision, and 401(k). Pay is based on factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience and skillset, and location. Pay and benefits are subject to change at any time, consistent with the terms of any applicable compensation or benefit plans.
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New York, United States
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