Senior Product Marketing OPS

You will define the standards, tooling, and architectural patterns that allow data scientists and analysts to safely and efficiently ship models that directly power our credit and business decisions.

What you’ll work on:

• Own ML systems & tooling in production :
• Define and evolve ML platform architecture, including model registries, feature pipelines, training infrastructure, and inference services.
• Evaluate and introduce tooling that improves developer velocity, reproducibility, and safety across the ML stack.
• Architect, implement, and deploy ML/DL models into production environments.
• Ensure models are optimized for scalability, latency, and reliability.
• Automate Monitoring & Maintenance:
• Design and build automated monitoring systems to track model performance, drift, and data quality of ML/DL models that consume data from various sources.
• Establish alerting and retraining pipelines to maintain model performance and robustness sustainably over time .
• Automate Data Science processes:
• Develop frameworks to automate recurrent Data Science workflows (e.g. model evaluation, and retraining).
• Standardize best practices across the team for reproducibility and efficiency.
• Collaborate with technical teams & Lead other team members:
• Partner with Product, Engineering, and Risk teams to align ML/DL solutions to be productionized with business goals. Although you won’t be developing the models first hand initially, you will be involved with in-sample, out-of-sample, and production testing.
• Mentor junior and senior data scientists and analysts, fostering a culture of innovation, experimentation, and excellence.
• Research & Innovate in the MLOps spectrum:
• Stay ahead of emerging ML & DL production techniques and technologies, evaluating their applicability to organizational challenges.
• Drive experimentation and prototyping of novel production and automation approaches.

Requirements

Who you are:

• Background:
• You have at least five (5) years of experience with machine and deep learning engineering in a practical setting.
• You have a good understanding of fintech products, and risk management to interpret business data effectively.
• Technical expertise:
• You have strong programming abilities (structured, object-oriented, and/or event-oriented programming) and are comfortable programming in Python/R and SQL (with a focus on Snowflake, preferably).
• You have strong proficiency in ML/DL frameworks in Python (e.g. Tensorflow, PyTorch, Scikit-learn).
• You are comfortable consuming data through APIs, SFTP, or straight-up CSVs.
• You are experienced with MLOps tools (e.g. MLflow, Kubeflow, Docker, Kubernetes, AWS microservices).
• You have a solid understanding of cloud platforms, preferably AWS, distributed computing, and version control using GitHub & GitLab.
• You have a strong understanding of model serving patterns (batch vs. online, synchronous vs. asynchronous).
• You have experience designing feature pipelines with clear ownership, freshness guarantees, and backfills.
• You understand data engineering practices for ETL pipelines development, and datawarehouses/datalakes management.
• Leadership & Business acumen:
• You have a data-oriented mindset: you care about getting to the bottom of how to make decisions based on data.
• You have stakeholder management experience, keeping everyone up-to-date with key findings and explaining in a non-technical way results, methodologies and processes for data-driven decision making.

• Remote Work
• Contractor agreement
• Language classes