What you'll be doing:
Fraud is something we encounter on a regular basis and, as a Fraud Analyst, you will be one of Enova’s most valuable resources. You will develop, enhance and test the company’s rules, models and operational processes for use in determining the appropriate lending criteria and verification procedures, specifically related to preventing fraud. You may be asked to conduct ad hoc analysis using statistical and financial tools to recommend fraud prevention strategies. You will demonstrate the ability to interpret and organize data, and communicate it effectively to cross functional teams to solve business problems, provide requirements and support implementation. You will work with senior management to develop key performance indicators (KPI's) to ensure that our fraud prevention schemes are performing optimally and communicating insights through both presentations and write-ups of results and recommendations.
What you should have:
- A Bachelor's degree, Master’s or PhD in a quantitative field
- 3-7 years of experience in analytics or related field
- 2+ years of experience using statistical and/or machine learning models focused on fraud
- Advanced programming skills in Python, R or similar language and the ability to write customized programs for meaningful data analysis
- Experience working with relational databases, such as SQL
- Knowledge of statistical/predictive modeling
Analytics Team Overview:
Enova's Analytics team consists of over 60 quantitative professionals dedicated to using the latest cutting-edge techniques to drive business value. We are a shared service for the entire company and operate in four core Analytics workgroups:
- Portfolio Analytics – This team focuses on building cutting edge risk, pricing, and underwriting models to optimize our lending decisions by using advanced modeling and simulation techniques to optimize the performance of our loan products and operations.
- Fraud Analytics – Analysts on the fraud team use advanced data mining techniques to identify and fight online fraud.
- Marketing Analytics – The Marketing Analytics team is focused on applying statistical analysis and predictive modeling to help our marketing teams acquire and retain more valuable customers.
- Research, Architecture and Platforms - The RAP team builds and maintains all of our technical tools and platforms. They help investigate new analytics methodologies, use cases, and data sources, to institute new and best practices within the department.
At Enova we have a company-wide culture that emphasizes data-driven analysis. That means you spend less time presenting and more time with the fun part, crunching data. We are language agnostic, but primarily use Python, R, SAS, SQL and Mathematica. That means YOU get to pick the tool that works best for you and the analysis at hand.
Enova is a leading provider of online financial services that leverages its advanced technology and analytics to provide access to credit for non-prime consumers and small businesses. Our roots are in Chicago, but we have served nearly 5 million customers through our six businesses in the U.S. and abroad. We pride ourselves on hiring smart and driven people who bring new and innovative ideas to the table. Our philosophy is, "Life’s short. Work some place awesome."
Many of us consider our people to be Enova’s best perk. But to sweeten the deal, we also have a pretty awesome list of conventional (and less conventional) perks and benefits including competitive salaries, health care benefits, a 401K matching plan, a revamped parental leave program (and brand new nursing rooms for our returning mothers!) summer hours, tuition reimbursement and a sabbatical program. And of course we also have the things you’d expect at a leading tech company in Chicago, such as the snacks, game room, onsite massages/barbers/nail technicians, monthly social events, and sporting sponsorships.
Our goal at Enova is to recruit, hire, develop and maintain a diverse workforce. It is our policy to provide equal employment opportunity for all persons and not discriminate in employment decisions by placing the most qualified person in each job, without regard to any other classification protected by federal, state, or local law.