Fraud and Risk Specialist
- Status
- Open
- Remote policy
- Not stated
- Employment type
- Not stated
- Salary
- Not stated
- Categories
- PII Fraud Ops
- Source
- lyft
- First observed
- 2026-09-08 19:50 UTC
- Last seen
- 2026-09-08 19:50 UTC
- Source claims posted
- 2026-09-08 17:48 UTC
- Consecutive misses
- 0 of 3
What the posting says
At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.
Trust and confidence is fundamental to the Lyft marketplace, and the Fraud and Risk Analytics team is charged with providing and ensuring that trust and safety to all customers. The Fraud and Risk Analytics team is one of the most critical teams that helps protect Lyft and enables the company to grow in a sustainable way. The team is fast-paced, high-energy, and meticulous in diagnosing emerging fraud patterns and preventing fraud loss before it can happen. We drill holes in all the products we launch and obsess over how to make our business impervious to fraud vectors. The team conducts a rigorous analysis of complex data sets and sets up multiple layers of business rules, models, and other processes to prevent potential fraud. We are a highly cross-functional team and regularly engage in discussion and reviews with stakeholders to prioritize plans for reducing fraud impact and introducing safety features.
We’re looking for a rock-star to join a fast-paced environment to contribute to Fraud-related analysis and operations. This individual is responsible for investigating and developing solutions to prevent and mitigate third party fraud. This person will proactively identify fraud patterns and sources to minimize the company’s exposure to financial and reputational risk.
This person is quantitatively driven, detail-focused, and operations-savvy while ensuring the best possible customer experience. This individual possesses a high level of subject matter expertise and is experienced in utilizing information generated from fraud patterns, data analytics and business knowledge to identify insights and formulate a response plan. The individual will also work with the operations team to define efficient processes and systems for the fraud and customer support agents to resolve fraud issues quickly and meet organizational service-level objectives.
Responsibilities:
Conduct meticulous daily reviews of fraud queues to minimize financial loss and maintain organizational service-level objectives.
Take full ownership of performance outcomes by clarifying objectives and utilizing sound business judgement to formulate proactive solutions.
Serve as a rotating on-call specialist responsible for managing complex internal escalations across multiple communication channels.
Independently analyze and monitor false positive trends to iteratively refine business rules and fraud detection models.
Engage with Analytics, CX, and Trust stakeholders to resolve false positives and ensure a seamless experience for legitimate community members.
Partner with leadership to provide constant feedback, leveraging a trial-and-error approach to strengthen fraud protections and reduce friction.
Experience:
BA/BS; degrees in analytical fields such as Computer Science, Statistics, Operations, Business Administration, a plus.
1-2 years of experience in a fraud or risk monitoring role at a payments company, bank, or online marketplace.
A strong passion for Lyft’s mission and the ride sharing community.
Detective-like mentality; you want to help customers but also maintain strong risk protections.
Ability to spot patterns, solve problems, and identify things that just don’t look right.
Beginner to Intermediate experience in SQL a plus.
Have a bias towards action in resolving issues and perform in a high-energy, fast-paced environment
Ability to provide a high-level of detail in every action
History of effectively working with multiple teams to build new capability and implement projects
Strong time management skills
Benefits:
Great medical, dental, and vision insurance options with additional programs available when enrolled
Mental health benefits
Family building benefits
Child care and pet benefits
401(k) plan with company match to help save for your future
In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off
18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible
Subsidized commuter benefits
Monthly Lyft credits and complimentary Lyft Pink membership
Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, age, genetic information, or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law.
Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule following the establishment of a Lyft office in Nashville — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid
The expected base pay range for this position in the Nashville area is $49,200 - $61,500, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.
Quality
- x Salary range stated weight 35%
- x Remote policy stated weight 20%
- + Location stated weight 15%
- + Organisation stated weight 15%
- + Publication date stated weight 15%
Not enough history yet to judge honesty signals.
Timeline
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#642094 2026-09-08 19:50 UTCPublished