樱花动漫

Job Overview

As part of our commitment to preventing fraud across all consumer lending products, we聽are expanding our Fraud Policy team, which utilizes cutting-edge AI technology to

detect and prevent fraud while minimizing impact on good customers. As a Risk Policy聽lead, you will collaborate with Risk Operations, Data Science, Product, Engineering and聽Compliance teams to design and implement fraud mitigation risk strategies. If you're聽passionate about solving real customer problems through decision science and聽analytics, we welcome you to join our talented team.

Responsibilities

This role will involve developing fraud risk policies for our innovative consumer lending products, with a focus on fraud detection and prevention for new money movement products and features. You will create end-to-end risk policies and drive alignment with cross-functional stakeholders. Additionally, you will manage fraud risk aspects of business initiatives and respond to fraud-related events and demands. Expertise in fraud risk, financial analytics, and innovative risk strategies is required to enable 樱花动漫's growth, expansion, profitability, and trust-enabled consumer experiences.

• Use advanced analytical techniques to mine massive scale account and transactional data to build/optimize fraud risk strategies/policies, and translate into actionable business plans
• Lead the full cycle of risk strategy/policy work, including identify opportunities and action plan, develop and test policy, launch policy to production and monitor the performance
• Build subject matter expertise in different types of risks among money movement offerings to optimize the balance between risk mitigation and growth in the 樱花动漫 ecosystem
• Team up with our data scientist, risk operations, product, data engineering, analytics teams closely to drive customer segmentations, develop innovative risk strategies, and perform portfolio analyses
• Test into and recommend 3rd party data providers to solve individual stolen identity, ATO and other types of exposure risk
• Design, implement, and monitor testing to explore new methodologies and estimate key metrics for our risk strategies
• Must have good judgement with the ability to think creatively and strategically

Qualifications

• Strong business acumen and exceptional analytical ability, with 4+ years of relevant experience and bachelors degree in quantitative fields such as Statistics, Mathematics, Finance, Data Science; or MS/PhD with 2+ years of relevant working experience Preferred experience with Fintech and fraud risk in the areas of consumer lending
• Working knowledge of the fundamentals of risk policy for consumer loans and line of credit including fraud detection and identity checks
• Demonstrated experience in designing experiments and using data-driven insights to solve business problems
• Proficient in statistical analysis tools like SQL, Excel, Python, R, and data visualization tools Familiarity with big data technologies such as Hive, Hadoop, and related concepts preferred
• Polished partnership and communication skills, with the ability to interact cross-functionally with both business and technical partners You tell stories with data to influence stakeholders and build consensus for plans of action
• You thrive in a fast-paced environment, and are responsive and decisive during periods of peak fraud activity
• Passion for analyzing data and an insatiable curiosity to understand complex business issues and proactively looking for business opportunities and drive new innovations

樱花动漫 provides a competitive compensation package with a strong pay for performance rewards approach. The expected base pay range for this position is:

Bay Area California $144,000 - $195,000

This position will be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at ).Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing pay equity for employees, 樱花动漫 conducts regular comparisons across categories of ethnicity and gender.