POSITION OVERVIEW:

We are growing our world-class team of mission-driven, entrepreneurial Data Scientists who are passionate about broadening financial inclusion by untapping insights from non-traditional data. Be part of the team responsible for developing and enhancing Oportun’s core intellectual property used in scoring risk for underbanked consumers that lack a traditional credit bureau score. In this role you will be on the cutting edge working with large and diverse (i.e. data from dozens of sources including transactional, mobile, utility, and other financial services) alternative data sets and utilize machine learning and statistical modeling to build scores and strategies for managing risk, collection/loss mitigation, and fraud. You will also drive growth and optimize marketing spend across channels by leveraging alternative data to help predict which consumers would likely be interested in Oportun’s affordable, credit building loan product. 

 

RESPONSIBILITIES: 

Develop data products and machine learning models used in Risk, Fraud, Collections, and portfolio management, and provide frictionless customer experience for various products and services Oportun provides. Build accurate and automated monitoring tools which can help us to keep a close eye on the performance of the models and rules. Build model deployment platform which can shorten the time of implementing new models. Build end-to-end reusable pipelines from data acquisition to model output delivery. Lead initiatives to drive business value from start to finish including project planning, communication, and stakeholder management. Lead discussions with Compliance, Bank Partners, and Model Risk Management teams to facilitate the Model Governance Activities such as Model Validations and Monitoring.

 

QUALIFICATIONS: 

A relentless problem solver and out of the box thinker with a proven track record of driving business results in a timely manner Master’s degree or PhD in Statistics, Mathematics, Computer Science, Engineering or Economics or other quantitative discipline (Bachelor’s degree with significant relevant experience will be considered). Hands on experience leveraging machine learning techniques such as Gradient Boosting, Logistic Regression and Neural Network to solve real world problems 3+ years of hands-on experience with data extraction, cleaning, analysis and building reusable data pipelines; Proficient in SQL, Spark SQL and/or Hive 3+ years of experience in leveraging modern machine learning toolset and programming languages such as Python Excellent written and oral communication skills Strong stakeholder management and project management skills Comfortable in a high-growth, fast-paced, agile environment  Experience working with AWS EMR, Sage-maker or other cloud-based platforms is a plus Experience with HDFS, Hive, Shell script and other big data tools is a plus

 

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