Summary of Role : This position will be a part of the Trust & safety team, where the candidate will be working closely with cross functional teams including Business, Data Science, Product, Eng & Finance by bringing together right elements w.r.t fraud identification & remediation.  Key responsibilities:  * Online fraud detection such as collusion, multiple ids, bonus abuse etc. using data analytics.  * Use analytics techniques to detect, prevent and mitigate fraud. Fraud hypothesis and identifying new patterns of abuse * Work on Insights and help in translating data into actionable business decisions. * Solutioning with Business & Product team by bringing in the right process/ system/ policy fixes in a manner that balances b/w cost & experience * Defining key Financial and Operational metric to enable real time detection of fraud/ abuse * Own daily, weekly and monthly reporting of KPIs. * Build strategies and suggest changes in processes which will eventually become industry benchmarks in the risk management space. * Analyzing behavior of new users and old users for identifying new patterns which may help in early detection of risk. * Explore new features, external engagements and features/variables for better Risk mitigation. * Experience of 3+ years in the field of Analytics preferably risk analytics for app based product * Engineering graduates from IIT/NIT or any good college. * Strong analytical thought process and aptitude for creative problem solving * Working knowledge of database tools is must * Hands on experience in SQL/HQL/Hive * Experience in one or more is a plus : Python, Spark, R, Scala * Working knowledge of Power BI or Tableau for data visualization and KPI measurement "We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regards to race, color, religion, sex, disability status, or any other characteristic protected by the  We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, colour, religion, sex, disability status, or any other characteristic protected by the law.