Role Overview
Are you a passionate and experienced Machine Learning Engineering Manager with a strong background in payments, cards, and lending? We’re looking for someone like you to join our growing fintech company and lead the charge in driving innovation through machine learning!
As the Machine Learning Engineering Manager, you will lead a team of talented data scientists and machine learning engineers, building and deploying advanced machine learning models that directly impact our product offerings in the payments, cards, and lending spaces. You’ll collaborate with cross-functional teams to design cutting-edge solutions that drive business outcomes while ensuring the scalability, reliability, and performance of our machine learning systems.
Key Responsibilities:
- Team Leadership: Manage, mentor, and develop a high-performing team of data scientists and machine learning engineers, ensuring both their technical and professional growth.
- Machine Learning Model Deployment: Oversee the end-to-end lifecycle of machine learning models from development to deployment in production, ensuring they deliver measurable business value.
- Impact Measurement: Use techniques such as A/B testing to measure and demonstrate the impact of machine learning models on business metrics and user experiences.
- Hands-on Coding: Write high-quality production code in Python and SQL, and contribute to technical solutions and model development when necessary.
- Stakeholder Communication: Communicate complex technical findings clearly and effectively to non-technical stakeholders, helping them understand how machine learning solutions contribute to the company’s business goals.
- Innovation & Roadmap: Proactively influence the machine learning roadmap, bringing new ideas and a bias for impact to drive meaningful improvements in the product.
- Research & Experimentation: Keep up-to-date with the latest trends and research in machine learning, applying new technologies and approaches to drive continuous improvement.
- Containerization Expertise: Experience with containers and container orchestration technologies such as Kubernetes, Docker, and/or Mesos, including lifecycle management of containers.
- Domain Knowledge: Strong domain knowledge in credit risk and/or the payments industry, including a deep understanding of financial services and lending models.
