Associate Data Scientist (Fraud Strategy)

Lending Club

San Francisco (CA)

Hybride

USD 120 000 - 170 000

Plein temps

Il y a 4 jours
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Avantages offerts par ce poste

Untracked vacation
Hybrid work model
Health benefits
Parental leave
Mothers Rooms
Wellness program
Volunteer time
Onsite gym
Barista cafe
Snack pantry

Résumé du poste

Lending Club in San Francisco is seeking an Associate Data Scientist to 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.

Analyze large, complex datasets using SQL and Python to identify fraud patterns, emerging risks, and opportunities to improve decision-making; support the development and implementation of fraud

Qualifications

  • Hands-on experience using SQL and Python to work with large datasets and produce reproducible analysis.
  • Familiarity with AI tools and their strengths/limits for real-world analytical problems.
  • Experience in data science, fraud analytics, or related analytical fields.
  • Ability to translate analytical findings into clear recommendations for technical and business audiences.

Responsabilités

  • Analyze large, complex datasets to identify fraud patterns and emerging risks.
  • Support development and implementation of fraud detection strategies and models.
  • Build and maintain reliable queries, dashboards, and reporting.
  • Monitor fraud trends, loss metrics, strategy performance, and investigate anomalies.
  • Develop repeatable processes to monitor model and strategy performance, data accuracy, and changes in population behavior.
  • Partner with cross-functional teams to translate business questions into analytical approaches and support solutions from analysis through implementation.
  • Use AI-assisted tools where appropriate to accelerate exploration, coding, and documentation while validating outputs and protecting sensitive data.

Connaissances

SQL
Python
AI tools familiarity
Statistics
Data visualization
Model monitoring
Communication
Ownership

Formation

Bachelor's degree or higher

Outils

Tableau

Description du poste

  • 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
  • Analyze large, complex datasets using SQL and Python to identify fraud patterns, emerging risks, and opportunities to improve decision-making
  • Support the development and implementation of fraud detection strategies, rules, and models based on data analysis and business requirements
  • Monitor fraud trends, loss metrics, strategy performance, and key indicators, investigating anomalies and translating findings into actionable insights
  • Build and maintain reliable queries, dashboards, and reporting using established data and business intelligence standards
  • Evaluate new internal and external data sources, features, and analytical techniques for predictive value, data quality, and practical application
  • Develop repeatable processes to monitor model and strategy performance, data accuracy, and changes in population behavior
  • Partner with cross-functional teams to translate business questions into analytical approaches and support solutions from analysis through implementation
  • Use AI-assisted tools where appropriate to accelerate exploration, coding, and documentation while validating outputs and protecting sensitive data
Benefits
  • Untracked vacation for salaried employees, which means take it when you need it, and generous paid time off for hourly team members
  • A hybrid work model that balances in-office and work from home
  • Leading health and safety protocols related to COVID, including complimentary at-home testing kits from Cue Health for those coming to the office
  • Up to 16 weeks paid leave for new parents, plus a fully paid, phased return-to-work policy for qualified leaves
  • Mothers Rooms and hospital-grade pumps in every LC office
  • Generous and varied mental health benefits
  • Wellness program with cash incentives — earn up to $75 per month for being active
  • Paid volunteer time and donation matching
  • Onsite gym, locker room, bike room, and fitness classes (depending on location)
  • Café with our own in-house barista
  • Stocked pantries with healthy snacks and drinks
  • Technology-forward and collaborative office spaces
  • You have practical familiarity with AI tools, understand their strengths and limitations, and are ready to apply that judgment to real-world analytical problems
  • Working knowledge of statistical analysis, data visualization, and model or strategy performance measurement
  • You collaborate with humility, adapt to changing priorities, and bring curiosity, integrity, and a problem-solving mindset
  • You use sound judgment when handling sensitive data and applying new methods, balancing speed with accuracy, explainability, and risk
  • Hands-on experience using SQL and a statistical programming language such as Python to work with large datasets and produce reproducible analysis
  • Bachelor’s degree or higher, or equivalent combination of education and experience
  • Ability to translate analytical findings into clear recommendations for technical and business audiences
  • You take ownership of assigned work, ask thoughtful questions, and incorporate feedback to improve quality and outcomes
  • 2+ years of experience in data science, fraud analytics, decision science, or a related analytical role
  • 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
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