- Happen Bank, N.A.
- San Francisco, CA
- Full-Time
- <72 Hours
Associate Data Scientist, Fraud Strategy.
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Associate Data Scientist, Fraud Strategy: our view in 3 lines...
- The Role:This role is for an associate data scientist focused on fraud strategy at a bank.
- The Person:The person will analyze large datasets, build and monitor fraud detection strategies, maintain queries and reporting, evaluate data sources, and partner with cross-functional teams on analytical solutions.
- Requirements:The ideal candidate has 2+ years of experience, a bachelor’s degree or higher, hands-on experience using SQL and Python, and working knowledge of statistical analysis, data visualization, and model or strategy performance measurement.
About the role
Current Employees of Happen Bank: Please apply via your internal Workday Account
Happen Bank (formerly LendingClub) is built around a simple purpose: to clear the way to help people turn intention into action, and action into financial progress. That means offering focused products, a frictionless mobile-first experience, and clear terms with no gotchas. Respect and fairness is part of our DNA, and that ideal shapes how we work, how we treat each other, and how we invest in our employees and our community. Join us in using data, bold thinking, and a commitment to innovation to help clear the way for millions of Americans to achieve more.
About the Role
The Fraud and Data Science team uses internal and external data to detect, prevent, and mitigate fraud across Happen Bank. As an Associate Data Scientist, you’ll turn complex data into actionable insights, develop analytical solutions that strengthen fraud strategies, and partner across Fraud, Risk, Product, Technology, and Operations while building deeper technical and business expertise.
What You'll Do
- Analyze large, complex datasets using SQL and Python to identify fraud patterns, emerging risks, and opportunities to improve decision-makingÂ
- Support the development and implementation of fraud detection strategies, rules, and models based on data analysis and business requirementsÂ
- Monitor fraud trends, loss metrics, strategy performance, and key indicators, investigating anomalies and translating findings into actionable insightsÂ
- Build and maintain reliable queries, dashboards, and reporting using established data and business intelligence standardsÂ
- Evaluate new internal and external data sources, features, and analytical techniques for predictive value, data quality, and practical applicationÂ
- Develop repeatable processes to monitor model and strategy performance, data accuracy, and changes in population behaviorÂ
- Partner with cross-functional teams to translate business questions into analytical approaches and support solutions from analysis through implementationÂ
- Use AI-assisted tools where appropriate to accelerate exploration, coding, and documentation while validating outputs and protecting sensitive dataÂ
About You
- 2+ years of experience in data science, fraud analytics, decision science, or a related analytical roleÂ
- Bachelor's degree or higher, or equivalent combination of education and experienceÂ
- Hands-on experience using SQL and a statistical programming language such as Python to work with large datasets and produce reproducible analysisÂ
- Working knowledge of statistical analysis, data visualization, and model or strategy performance measurementÂ
- Ability to translate analytical findings into clear recommendations for technical and business audiencesÂ
- You take ownership of assigned work, ask thoughtful questions, and incorporate feedback to improve quality and outcomesÂ
- You use sound judgment when handling sensitive data and applying new methods, balancing speed with accuracy, explainability, and riskÂ
- You have practical familiarity with AI tools, understand their strengths and limitations, and are ready to apply that judgment to real-world analytical problemsÂ
- You collaborate with humility, adapt to changing priorities, and bring curiosity, integrity, and a problem-solving mindset
Nice to Have
- Experience in fraud, credit risk, financial services, e-commerce, or another high-volume digital environment
- Experience with Tableau or another business intelligence and visualization platform
- Exposure to feature engineering, predictive modeling, model monitoring, or experimentation
Work Location Â
San FranciscoÂ
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The above locations are eligible offices for this role. The locations have been determined to foster in-person collaboration with this role’s team or the related business lines. We utilize a hybrid work model, and our teams are in-office Tuesdays, Wednesdays, and Thursdays. In-person attendance is essential for this role’s success, and remote placement will not be considered. Happen Bank offers relocation, based on actual job level. Â
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Time Zone Requirements Â
Local hours (PT)Â
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While the position will primarily work local hours, Happen Bank is headquartered in Pacific Time and our ideal candidate will be flexible working across time zones when necessary.Â
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Travel Requirements Â
As needed travel to Happen Bank offices and/or other locations, as needed. Â
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Compensation Â
The target base salary range for this position is 125,000-145,000. The base salary of the role will be determined by job-related knowledge, experience, education, skills, and location. Base salary is just one part of Happen Bank's Total Rewards package. You may also be eligible for long-term awards (equity) and an annual bonus (which is based on company performance, employee performance and eligible earnings).Â
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We’re creating new financial services solutions for our members based on fairness, simplicity, and heart, and we treat our employees the same way. We offer a competitive benefits package that includes medical, dental and vision plans for employees and their families, 401(k) match, health and wellness programs, flexible time off policies for salaried employees, up to 16 weeks paid parental leave and more. Â
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#LI-HybridÂ
#LI-AH1Â
Happen Bank is an equal opportunity employer and dedicated to diversity, equity, and inclusion in the workplace. We do not discriminate on the basis of race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), gender, gender identity, gender expression, sexual orientation, age, marital status, veteran status, disability status, political views or activity, or other applicable legally protected characteristics. We believe that a variety of perspectives will make our teams and business stronger as we work together to transform the traditional banking system.Â
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We are committed to providing reasonable accommodations for qualified individuals with disabilities in our job application process. If you need assistance or an accommodation due to a disability, please contact us at interviewaccommodations@happen.com.Â
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Notice on AI Tool UseÂ
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For select roles and locations, candidate interviews may be recorded, transcribed and summarized by tools such as artificial intelligence (AI) to assist our hiring managers with the application process.
You will have the opportunity to opt out of recording, transcription, and summarization prior to any scheduled interviews. We will not discriminate against you if you choose to opt out.
During the interview, we will collect the following categories of personal information from or about you: contact information, identifiers, professional and employment-related information, sensory information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment.
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We will only share your interview, transcription, or summary with persons whose expertise or technology is necessary to process your application, evaluate your fitness for a position, and administer or support the tool. We will not sell your personal information or disclose it to any third party for their marketing purposes. For more information about how we will handle your personal information, please refer to our Privacy Disclosure.
We will delete any recording of your interview promptly but in no event later than 30 days after making a hiring decision.

