Fraud and Risk Analyst

The Senior Statistical Data Analyst is responsible for designing unique analytic approaches to detect, assess, and recommend the optimal customer treatment to reduce frictions and enhance experience while properly managing fraud risk with data driven and statistical methods. You will analyze large amounts of account and transaction data to build customer level insights to derive the recommendations and methods to reduce friction and enhance experience on fund availability, transaction/fund hold time and more, and models while managing the customer

experience. This role requires critical thinking and analytical savviness to work in a fast-paced environment but can be a rewarding opportunity to help bring a great banking experience and empower the customers to achieve their financial goals.

Responsibilities:

• Analyze large amounts of data/transactions to derive business insights and create innovative solutions/models/strategies.
• Aggregate and analyze internal and external risk datasets to understand performance of fraud risk at customer level.
• Analyze customer's banking/transaction behaviors and be able to build predictive models (simple ones like logistic regression, linear regression) to predict churns or negative outcomes or running correlation analysis to understand the correlation.
• Develop personalized segmentations and micro-segmentation to identify customers based on their fraud risk, banking behavioral, and value.
• Conduct analysis for data driven recommendations with reporting dashboard to optimize customer treatment regarding friction reduction and fund availability across the entire banking journey.

Skillset:

• Analytics professional preferably with experience in

  Fraud analytics

  .
• Strong knowledge and working

  experience in SQL and Python is a must.

• Experience analyzing data with

  statistical approaches

  with python (e.g. in Jupyter notebook): for example, clustering analysis, decision trees, linear regression, logistic regression, correlation analysis
• Knowledge of

  Tableau and BI

  tools
• Hands-on use of AWS (e.g. S3, EC2, EMR, Athena, SageMaker and more) is a plus

• Strong communication

  and interpersonal skills
• Strong

  knowledge of financial products

  , including debit cards, credit cards, lending products, and deposit accounts is a plus.
• Experience working at a

  FinTech or start-up

  is a plus.