About Vesta
At Vesta, we envision a world without e-commerce fraud. We’re passionately pursuing that future by building industry leading SaaS solutions for fraud protection and guaranteed e-commerce payments to a global customer base. We are headquartered in Portland, Oregon with offices in the US, Singapore, Ireland, San Francisco, and Mexico. This role can be located near one of our hubs in the Bay Area, Atlanta area, or Washington DC.
About the Role
As Vice President of Machine Learning Engineering, you will lead the engineering aspects of Vesta’s machine learning efforts, including the development and implementation of Vesta’s next generation machine learning platform. As part of the larger machine learning and artificial intelligence organization, you’ll play an instrumental role in delivering innovative solutions to our global customer base – helping them drive the cost of fraud to zero while maximizing their revenues. You’ll lead a team of talented machine learning engineers and data scientists who are using cutting edge methods to detect fraud, mitigate fraud attacks. As the lead of MLE, you will ensure that our models and model delivery is fast, reliable and highly scalable. You’ll also work with other teams throughout the company, including Product, Engineering and Operations teams so that we can deliver the best results for our customers.
Core Responsibilities
- Lead a team of machine learning engineers that build and operate our fraud detection and approval optimization systems.
- Recruit high caliber technical talent, providing hand-on coaching and technical mentorship to the team.
- Play a leadership role in all stages of a machine learning solution: from ideation, through model development through monitoring and updating of models live in production.
- Together with Product, Data Science and other teams, design, build and deliver our next-generation machine learning solutions.
- Focus on our customers and identify new areas where machine learning and related technologies can create better customer outcomes.
- Create our ML infrastructure, including data & model training pipelines, MLOps and monitoring/anomaly detection.
- Write high quality code using software development best practices (testing, CI/CD, DevOps/MLOps). Build a team culture where everyone works together to improve.
Education & Skills
- 10+ years of relevant experience in data science or machine learning: you’ve shipped ML solutions on the Cloud using tools like Python, SQL, Spark, scikit-learn, Tensorflow, or similar. Bonus for experience with Microsoft Azure.
- Experience leading machine learning engineering teams: you believe that coaching your team is one of your most important priorities and your key to success.
- Experience in the full ML lifecycle: from problem understanding, to data wrangling, model development, deployment through monitoring and regular model refits.
- Experience leading engineering efforts across the machine learning lifecycle, including: from building data and feature pipelines to model training, deployment and scoring through monitoring of models in production.
- High bar for code quality and engineering best practices, write clean, performant, and tested code and deploy using best practices in CI/CD, DevOps, MLOps.
- Experience with both supervised and unsupervised machine learning methods.
- Ideal: Experience working with fraud detection, particularly in the financial services industry. Alternatively, experience in similar adversarial problems, such as anti-money laundering, or cybersecurity.