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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal Data Scientist, Operations - **Company:** Mission Lane - **Location:** Richmond, VA, United States - **Experience:** Experienced - **Salary:** $173,000.0 - $203,000.0 - **Contract:** Permanent contract - **Skills:** Airflow, Code Review, Python (Programming Language), NumPy, Software Engineering, Supervised Learning, Google Cloud, Test-Driven Development (TDD), Apache Spark, Scikit Learn, Kubernetes, Xgboost, Machine Learning Operations, Code Restructuring - **Published:** September 26, 2026 - **Apply:** https://www.juju.com/job/16_93c30948 ## About the Role * A PhD in a quantitative field and 3+ years of experience in a related role, or a BS/MS in a quantitative field and 7+ years of experience in a related role * Has created, deployed, and managed supervised learning models in production systems for vital applications * Shares best practices for software engineering and can help experienced data scientists work through complex technical problems, especially operationalizing and evaluating models for real-world use * Practices solid software engineering fundamentals (test-driven development, code review, refactoring) and works fluently in our core tech stack * Interested in a wide range of ML tooling, from established tools (Spark, Kubernetes, Airflow, MLFlow) to emerging ones (Chalk, BentoML, DVC) * Experience assuming project leadership on a workstream, working independently with guidance focused on priorities and key objectives * Ability to travel ~4+ times per year for high quality in-person collaboration ## Description We're looking for a Principal Data Scientist to be the technical anchor for Mission Lane's collections models, reporting to the Director, Data Science. The impact you'll make: Nobody wants to fall behind on a payment, but life gets in the way sometimes. How Mission Lane responds in those moments depends on the models you'll build. What helps someone move forward isn't the same from person to person. Some respond to a text, some need a conversation, some just need more time. Working out which is which is one of the most interesting open questions here, one the field hasn't fully solved yet. Mission Lane is young, but we've landed in an exciting, stable stretch of maturation. The collections data science practice specifically is still taking shape, so there's room to define what "good" looks like here. What you'll own: * Implementation of the data science roadmap for collections, serving as the primary technical point of contact day to day, and setting the standard for rigor and production quality across the data scientists, consultants, and contractors working alongside you * End-to-end models that anticipate how customers are likely to respond to different kinds of outreach, and that shape which approach Mission Lane uses and when * Durable internal modeling practices that reduce how much of this work needs to run through outside consultants over time Our core tech stack includes: Python and the PyData stack (numpy, polars, scikit-learn), LightGBM, DVC, Kubernetes, Airflow, Google Cloud, MLFlow, BentoML, and Chalk, our feature store. You'll thrive in this role if: * You adapt quickly to a new domain. You don't need collections experience in order to pick up the business context fast and connect it to the technical problem. * You can trace a modeling choice back to the business problem it's solving, keeping the modeling technique in service of the goal. * You can explain your reasoning clearly enough that consultants, contractors, and full-time teammates alike can pick up your standard and run with it. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Fully Orchestrating Databricks from Airflow](https://www.wearedevelopers.com/videos/336-fully-orchestrating-databricks-from-airflow) - [Shoot for the moon - machine learning for automated online ad detection](https://www.wearedevelopers.com/videos/502-shoot-for-the-moon-machine-learning-for-automated-online-ad-detection) - [Vectorize all the things! Using linear algebra and NumPy to make your Python code lightning fast.](https://www.wearedevelopers.com/videos/562-vectorize-all-the-things-using-linear-algebra-and-numpy-to-make-your-python-code-lightning-fast) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) ## Related Articles - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)