Simpl - Data Scientist - Fraud Analytics

The thrill of working at a start-up that is starting to scale massively is something else.

Simpl (getsimpl.com) was formed in 2015 by Nitya Sharma, an investment banker from Wall Street and Chaitra Chidanand, a tech executive from the Valley, when they teamed up with a very clear mission - to make money simple, so that people can live well and do amazing things. Simpl is the payment platform for the mobile-first world, and we- re backed by some of the best names in fintech globally (folks who have invested in Visa, Square and Transferwise), and has Joe Saunders, Ex Chairman and CEO of Visa as a board member.

Everyone at Simpl is an internal entrepreneur who is given a lot of bandwidth and resources to create the next breakthrough towards the long term vision of - making money Simpl- . Our first product is a payment platform that lets people buy instantly, anywhere online, and pay later. In the background, Simpl uses big data for credit underwriting, risk and fraud modelling, all without any paperwork, and enables Banks and Non-Bank Financial Companies to access a whole new consumer market.

Job Description :

- Simpl is building a highly efficient multi dimensional fraud team. The fraud team consists of people from different domains like engineering, data sciences, operations, products etc with a single objective to fight fraud.

- As a data scientist in the team you would be responsible for

- Analysing and finding new fraud patterns

- Design, develop and evaluate predictive models to flag suspicious users based on found pattern

- Quantifying the impact of your models on business and evangelising it

- Working with other team members of fraud team with an objective to have a complete feedback loop and give a good user experience to the end customers who were falsely flagged

Required skills - Non Negotiable

- Good programming skills with clarity in fundamentals - preferably python

- Proficient with SQL and relational databases

- Proficient with git (https://guides.github.com/introduction/git-handbook/)

- Understanding of statistics and model building techniques

- Familiarity with AWS infrastructure (EC2, EMR, Redshift, RDS, Redis, Kafka)

- Good communication skills