Join a leading fintech company that’s democratizing finance for all.
Robinhood Markets was founded on a simple idea: that our financial markets should be accessible to all. With customers at the heart of our decisions, Robinhood and its subsidiaries and affiliates are lowering barriers and providing greater access to financial information. Together, we are building products and services that help create a financial system everyone can participate in.
With growth as the top priority...
The business is seeking curious, growth-minded thinkers to help shape our vision, structures and systems; playing a key-role as we launch into our ambitious future. If you’re invigorated by our mission, values, and drive to change the world — we’d love to have you apply.
About the team + role
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Insights from data power most decisions at Robinhood and our company trajectory is defined by the systems, tools, and analytics powered by this exceptional team. As a Machine Learning Engineer working on Fraud and Risk, you would work with backend engineers, product managers and operations teams across the company to understand and mitigate the risks to our business.
Robinhood faces unique data challenges with a focus on integrating complex data streams such as rapidly changing market data, user data based on app activity, and brokerage operations data to understand user behaviour and the risks to our business.
We are looking for a Machine Learning Engineer to help detect and reduce risk to Robinhood - a crucial role to our business and customers. The ideal candidate is passionate about understanding the different fraud vectors at a fast-growing company and building solutions to mitigate these risks. This team is part of the larger Data Team here at Robinhood.
What you’ll do
- Combining knowledge of several research domains to improve our understanding of different risks to Robinhood and help power decisions
- Designing new machine learning systems to power the fraud prevention and risk reduction efforts at Robinhood especially in product areas
- Build production grade models on large-scale datasets to measure effectiveness across products by leveraging statistical modeling, machine learning and data mining techniques.
- Collaborate with the rest of the data team and partner marketing, product, content, design teams to build data solutions and products to drive user and revenue growth.
- Work with cross-functional teams to implement insights and analytical solutions to empower data-driven decision making.
- Problem solving skills and a can-do attitude to dive deep into data to solve business problems
What you bring
- Familiarity with fraud domains and banking processes, including account takeover, ACH fraud, debit/credit card fraud, first-party fraud, and synthetic identity fraud.
- Demonstrated expertise in building and deploying fraud models using large datasets, with a strong track record of success in fraud detection and prevention.
- Knowledge of model risk governance and experience collaborating with model validation teams to ensure compliance with regulatory requirements.
- Graduate degree in a quantitative field such as mathematics, economics, statistics, engineering or natural sciences (or equivalent research experience)
- Solid understanding of unsupervised learning, statistical analysis and machine learning algorithms for imbalanced datasets.
- Excellent programming skills, including familiarity with either Python (numpy, scipy, pandas) or R programming languages
- Experience with Tableau, Looker, and/or Mode
- Experience communicating data driven insights
- 2 + years professional experience as a Machine Learning Engineer
- 1 year of experience working in a research setting in academic or commercial setting
- Passion for working and learning in a fast-growing company
- Strong customer empathy
- Intense sense of curiosity
We use Covey as part of our hiring and / or promotional process for jobs in NYC and certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on September 19, 2024.
Please see the independent bias audit report covering our use of Covey here.
Base pay for the successful applicant will depend on a variety of job-related factors, which may include education, training, experience, location, business needs, or market demands. The expected salary range for this role is based on the location where the work will be performed and is aligned to one of 3 compensation zones. This role is also eligible to participate in a Robinhood bonus plan and Robinhood’s equity plan. For other locations not listed, compensation can be discussed with your recruiter during the interview process.
Click here to learn more about available Benefits, which vary by region and Robinhood entity.
We’re looking for more growth-minded and collaborative people to be a part of our journey in democratizing finance for all. If you’re ready to give 100% in helping us achieve our mission—we’d love to have you apply even if you feel unsure about whether you meet every single requirement in this posting. At Robinhood, we're looking for people invigorated by our mission, values, and drive to change the world, not just those who simply check off all the boxes.
Robinhood embraces a diversity of backgrounds and experiences and provides equal opportunity for all applicants and employees. We are dedicated to building a company that represents a variety of backgrounds, perspectives, and skills. We believe that the more inclusive we are, the better our work (and work environment) will be for everyone. Additionally, Robinhood provides reasonable accommodations for candidates on request and respects applicants' privacy rights. Please review the specific Robinhood Privacy Policy applicable to the country where you are applying.