채용
Benefits & Perks
•Flexible work arrangements
•401(k) matching
•Professional development budget
•Competitive salary and equity package
•Team events and activities
•Flexible Hours
•Learning
•Equity
Required Skills
Python
React
JavaScript
Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest.
The Merchant Risk Analytics team works cross-functionally with Merchant Underwriting, Compliance, Engineering, and other analytics teams to establish scalable risk management for merchant acquisition and portfolio monitoring. We're looking for an intelligent, driven individual who wants to learn, grow, and solve challenging problems.
This role requires strong analytical foundations and a willingness to operate across the full merchant risk lifecycle from onboarding to ongoing monitoring and risk expansion. You will support the development and maintenance of scalable analytics, modeling, automation, and monitoring frameworks as Affirm’s merchant footprint grows. The role emphasizes hands-on execution, learning, and ownership of core analyses and model-driven workflows, while collaborating closely with cross-functional partners
WHAT YOU’LL DO
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Support merchant onboarding and ongoing portfolio monitoring through data analysis, modeling and automation workflows.
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Detect and monitor fraud across the network, responding quickly to emerging risks.
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Contribute to the development and improvement of scalable monitoring frameworks, controls, and reporting.
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Assist in identifying gaps and opportunities within existing risk processes.
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Partner cross-functionally to translate risk requirements into data-driven strategies
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Leverage AI-assisted tools to accelerate analysis, automate workflows, and support the development of scalable, agentic merchant risk solutions.
WHAT WE LOOK FOR
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EXPERIENCE - Minimum 1 year of experience in risk management or a related field, including prior experience with analytics.
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ANALYTICAL THINKING - Strong problem-solving skills with the ability to break down ambiguous problems using data.
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RESILIENCY - Ability to think creatively and critically, thriving in a fast-paced environment.
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CURIOSITY - Eagerness to explore new tools and methodologies to solve real-world problems.
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TECHNICAL SKILLS- Strong coding skills in Python, SQL or other scripting languages; familiarity with agentic / LLM orchestration frameworks.
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PRODUCT KNOWLEDGE - Passion for understanding how the product works and the broader context of business decisions.
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AI FLUENCY – Comfortable using AI tools to enhance productivity, analysis, and workflow automation; interest in building or leveraging agentic workflows to solve risk problems.
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EDUCATION - Bachelor’s degree in a quantitatively rigorous field such as engineering, statistics, optimization, math, finance, or economics.
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Pay Grade
-
I
Equity Grade - 5
**Employees new to Affirm typically come in at the start of the pay range.**Affirm focuses on providing a simple and transparent pay structure which is based on a variety of factors, including location, experience and job-related skills.
Base pay is part of a total compensation package that may include equity rewards, monthly stipends for health, wellness and tech spending, and benefits (including 100% subsidized medical coverage, dental and vision for you and your dependents.)
USA base pay range (CA, WA, NY, NJ, CT) per year: $115,000-155,000
USA base pay range (all other U.S. states) per year: $102,000-142,000
Affirm is proud to be a remote-first company! The majority of our roles are remote and you can work almost anywhere within the country of employment. Affirmers in proximal roles have the flexibility to work remotely, but will occasionally be required to work out of their assigned Affirm office. A limited number of roles remain office-based due to the nature of their job responsibilities.
We’re extremely proud to offer competitive benefits that are anchored to our core value of people come first. Some key highlights of our benefits package include:
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Health care coverage
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Affirm covers all premiums for all levels of coverage for you and your dependents
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Flexible Spending Wallets - generous stipends for spending on Technology, Food, various Lifestyle needs, and family forming expenses
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Time off - competitive vacation and holiday schedules allowing you to take time off to rest and recharge
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ESPP - An employee stock purchase plan enabling you to buy shares of Affirm at a discount
We believe It’s On Us to provide an inclusive interview experience for all, including people with disabilities. We are happy to provide reasonable accommodations to candidates in need of individualized support during the hiring process.
For U.S. positions that could be performed in Los Angeles or San Francisco Pursuant to the San Francisco Fair Chance Ordinance and Los Angeles Fair Chance Initiative for Hiring Ordinance, Affirm will consider for employment qualified applicants with arrest and conviction records.
By clicking "Submit Application," you acknowledge that you have read Affirm's Global Candidate Privacy Notice and hereby freely and unambiguously give informed consent to the collection, processing, use, and storage of your personal information as described therein.
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About Affirm
Reviews
3.4
10 reviews
Work Life Balance
3.2
Compensation
4.1
Culture
2.8
Career
2.3
Management
2.1
45%
Recommend to a Friend
Pros
Great benefits
Good pay and compensation
Smart people and great team members
Cons
Poor management and leadership issues
Layoffs and job insecurity
Lack of career development and guidance
Salary Ranges
24 data points
L2
L3
L4
L5
L6
M3
M4
M5
M6
L2 · Product Designer L2
0 reports
$152,100
total / year
Base
$60,840
Stock
$76,050
Bonus
$15,210
$106,470
$197,730
Interview Experience
2 interviews
Difficulty
3.0
/ 5
Duration
14-28 weeks
Experience
Positive 50%
Neutral 50%
Negative 0%
Interview Process
1
Application Review
2
Recruiter Screen
3
Technical Phone Screen
4
Onsite/Virtual Interviews
5
Team Matching
6
Offer
Common Questions
Coding/Algorithm
System Design
Behavioral/STAR
Technical Knowledge
Past Experience
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