Senior Data Analyst, Fraud and Risk

Key Skills

Analytical and Problem solving, Data Analytics, Data Engineering, Data science techniques, Effective communication skills, Fraud Risk, Product Management, Python Programming, R Programming, SAS, SQL

Job Description

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. As a Senior Analyst, you will take ownership over multiple discrete fraud segments to develop and execute a strategy which addresses fraud in the earliest stages of detection. You will define fraud prevention measures that are mindful of the impact on good user experience while inhibiting fraud actors’ ability to reach our platform.

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. This person will work closely with Product Managers, Data Scientists and Engineers to mitigate and eliminate all forms of fraud. The individual will also work closely with operations teams 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:

• Responsible for the design, development and management of real-time fraud rules and models directly and in collaboration with relevant teams
• Analyze business orgs effectiveness by supporting implementation teams within Lyft Business and across the entire company 
• Support cross functional teams to build-out critical dashboards and tools to measure effectiveness of payment process, operations and new programs
• Collaborate with Product Management, Data Science, Engineering and Operations to resolve identity issues and other escalations
• Deliver enhancements to Lyft fraud detection and mitigation, communicate and prioritize development in collaboration with product management and engineering teams
• Report overall status, primary business metrics, key issues and solution recommendations and plans to senior management and executives
• Establish business cases and projected ROI on investments to prioritize opportunities related to fraud program

Experience:

• 8+ years of Data Analytics experience across payment, marketplaces, e-commerce, retail, digital media or financial services industries. Fraud or Identity experience is preferable, especially in the merchant risk domain.
• Bachelor’s degree in a quantitative field or relevant work experience 
• Expertise in SQL, SAS, R, Python or other programming language to query data and perform analysis
• Have a bias towards action in resolving issues and perform in a high-energy, fast-paced environment
• Ability to execute projects independently with minimal oversight
• Strong communication skills; Clearly articulate complex analysis through presentations and writing
• Ability to prioritize competing projects among departments, history of effectively working with multiple teams to build new capability and implement projects
• Effectively manage senior executive asks and provide written and verbal communication to them
• Ability to ‘Dive Deep’ with a structured approach to problem solving
• Excellent cross-group collaboration skills, ability to impact and influence outcomes in matrixed environment
• Good organizational skills including prioritizing, scheduling, time management, and meeting deadlines

Benefits:

• Extended health and dental coverage options, along with life insurance and disability benefits
• Mental health benefits
• Family building benefits
• Access to a Health Care Savings Account
• In addition to provincial observed holidays, team members get 15 days paid time off, with an additional day for each year of service 
• 4 Floating Holidays each calendar year prorated based off of date of hire
• 10 paid sick days per year regardless of province
• 18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible