Back River Search Group

Sr. Data Scientist

Back River Search - San Francisco, CA

The Company:

Our client is dedicated to helping our members lead healthy financial lives. That’s why they offer an award-winning bank account that doesn’t charge unnecessary fees, gives members early access to their paychecks, and helps them save money automatically. Hundreds of thousands of people use their mobile app and debit card to make purchases, track spending, save for the future, and more. Their members love the product and use it daily.

Our client believes that the big banks fail to help their members achieve financial health - and in many cases work against it, charging hundreds of dollars in hidden fees and pushing products that drive people into debt. They don’t think it needs to be this way, so they’re out to beat them.

They have one of the most experienced management teams in Fintech and recently raised $70M (series C) funding round from top-tier investors to fuel they rapid growth. If you’re looking to join a small but fast-growing company with a beloved, daily-use product and an authentic mission that puts people first, our client wants to meet you.

The Role:

Our client is mining vast amounts of data to protect our members and ourselves from fraudsters. Their Data Scientists play an absolutely pivotal role in the company, creating the models that allow them to evaluate the risk of various types of banking transactions in realtime in unprecedentedly holistic, efficient, and accurate ways.


What You’ll Do

In this role, you’ll build and implement novel machine learning and deep learning algorithms to combat fraud and help scale the risk we take on. Specifically, you’ll:

  • Understand the fraud and risk processes inherent in our product and how to minimize the same.
  • Creatively leverage new and existing data to increase the effectiveness and efficiency of our decision-making infrastructure
  • Work with engineers to design machine learning solutions that operate quickly and effectively at scale
  • Partner with operatives to quickly respond to rapidly evolving threats
  • Make business recommendations to the executive and cross-functional teams (e.g. cost-benefit, forecasting, experiment analysis) with effective presentations of findings at multiple levels of stakeholders through visual displays of quantitative information
  • Help build the next generation of data products


Qualifications & Requirements

  • 4+ years industry experience developing machine learning models at scale from inception to business impact. Leadership opportunities also available
  • Risk, fraud detection experience preferred but not a requirement.
  • You've earned a: (a) PhD in Machine Learning, Statistics, Optimization, Physics, or related field, with experience building production-ready ML models and systems; or (b) MS in CS or related field with 3+ years of experience in implementing and deploying large scale ML solutions
  • Deep understanding of and experience of modern machine learning techniques such as classification, recommendation systems and other shallow learning techniques, data analytics, and statistical models
  • Deep experience with ML/DL toolkits: Tensorflow, scikit-learn, XGBoost, Numpy, Scipy, Pytorch. SparkML.
  • Strong programming skills - Python preferred (though open to R for candidates with deep Risk/Fraud background). And intermediate to advanced knowledge of SQL and ability to wrangle data from many disparate data sources
  • Experience developing and deploying machine learning and/or deep learning solutions into a production environment
  • Ability to build tools to monitor the performance of models in production and ability to differentiate the
  • The versatility to communicate clearly with both technical and non-technical audience
  • Demonstrated leadership and self-direction, and willingness to both teach others and learn new techniques
  • Deep learning experience preferred but not a requirement

  • Technologies we use: MySQL, R, PHP, Python, AWS, Snowflake, and Looker, among many others.


Posted On: Wednesday, January 23, 2019

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