Our team is looking for a Senior Data Scientist to join our data products team. This role has significant latitude to shape how modeling, evaluation, and ML infrastructure work here: the standards, tooling, and processes you help establish will influence how models get built for a long time to come.
This is intentionally a hybrid role. At many companies, data science and ML infrastructure are split into separate functions: data scientists author and train models, while a dedicated platform team owns deployment, observability, and retraining. At Rho, we need someone who can do both, someone who can build the model and reason clearly about how it gets deployed, monitored, and retrained in production. You’ll work on high-leverage problems like our transaction coding suggestion engine, OCR/document understanding pipeline, and RAG-based and agentic systems, while also helping build the underlying foundation: evaluation frameworks, model deployment and monitoring practices, and the infrastructure decisions that determine whether ML at Rho is reliable and scalable. This role requires genuine fluency in ML infrastructure and evals, not just modeling - you should be as comfortable discussing feature store design or eval harness architecture with data engineers as you are validating a model’s statistical soundness.
Technologies we use for data: Python, Snowflake, DBT, PostgreSQL, Kubernetes, MLflow, Terraform, Prometheus, Google Cloud Services, Omni, Hex, PowerBI