Senior Data Scientist/ML

Rho is the modern banking platform built for the AI era. Startups and growth-stage companies can open accounts in minutes, issue cards, manage expenses, pay bills, and close the books – all in one connected platform backed by real human support.

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

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  Help define and evolve Rho's ML/DS practices: how models get evaluated, deployed, monitored, versioned, and retrained

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  Design and implement evaluation frameworks and eval harnesses that give the company real confidence in model quality, before and after launch

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  Build and own high-impact models and analyses powering products like transaction coding suggestions, OCR/document understanding, and RAG/agentic systems

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  Make and document key ML infrastructure decisions (model registries, feature stores, serving patterns, monitoring/alerting for drift and degradation) in close partnership with data engineering

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  Set technical standards and best practices that help the ML/DS practice scale as the team grows

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  Translate ambiguous business problems into well-scoped modeling questions

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  Design and analyze experiments (A/B tests, causal inference) to validate model impact rigorously

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  Advocate for and drive adoption of ML infrastructure and tooling improvements as needs grow

• 6+ years of experience in data science, applied ML, or a related quantitative field, with a track record of shipping models into production

• Deep, hands-on understanding of ML infrastructure: model registries, feature stores, serving architectures, monitoring/observability for models, and retraining pipelines

• Strong experience designing evaluation frameworks and evals for ML systems;, including offline metrics and ongoing production evaluation

• Comfortable operating as a hybrid DS/infrastructure practitioner:, someone who doesn't hand a model off to a platform team and walk away, but who can own it end to end when needed

• Experience helping establish or mature ML practices, standards, or infrastructure within a team and company

• Strong programming skills in Python, with solid SQL

• Comfortable making infrastructure trade-off decisions jointly with data/platform engineers, and able to speak credibly on both the modeling and systems sides

• Experience with statistical modeling, machine learning techniques, and experiment design

• Excellent communication skills; able to influence technical direction and bring both technical and non-technical stakeholders along

• Experience with RAG (Retrieval-Augmented Generation) systems, vector databases, or building/deploying agents

• Experience with OCR, document understanding, or other unstructured data extraction problems

• Familiarity with workflow orchestrators such as Airflow, Dagster, or Prefect

• Experience with cloud ML platforms (GCP Vertex AI, AWS SageMaker, or similar)

• Comfort with containerization and Kubernetes for model deployment

• Experience with BI tools such as Omni or Power BI

• Background in fintech, banking, or financial services data

• Experience mentoring or growing a DS/ML team