Backed by Index, Kleiner Perkins and NEA.
About the role
Build hands-on machine learning expertise across the full ML lifecycle, from feature engineering and experimentation to model deployment. Take models from initial experimentation through production and evaluate their impact using real-world customer outcomes.
What they're looking for
- 7+ years of professional experience in machine learning, applied science, or software engineering for ML, including hands-on model development and deployment
- Hands-on experience designing, training, tuning, and deploying models, and measuring improvements in production performance or business metrics
- Strong ML and statistical fundamentals, including feature engineering, experiment design, model evaluation, and diagnosing why a model underperforms
- Strong understanding of the strengths, limitations, and applications for both traditional and modern ML methods, including gradient-boosted trees and neural networks
- Experience constructing training datasets and addressing label quality, data leakage, class imbalance, and generalization across time periods or populations
- Strong Python skills, SQL proficiency for working with training and evaluation data, and hands-on experience with ML frameworks such as PyTorch, scikit-learn, XGBoost, or equivalents
More about this role
We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam.
The Fraud Data team at Plaid builds the machine learning systems that power Plaid’s fraud detection products, leveraging insights from across Plaid’s network to help identify and stop fraud before it happens. Our team works across the full data science and machine learning lifecycle—from discovering new signals and experimenting with models to deploying and optimizing them in production. We...
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