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Rho is a modern banking platform for startups, offering account opening, card issuance, expense management, bill payment, and financial close—all integrated with human support.
You'll join as a Founding Senior Data Scientist, shaping Rho's ML and data science practices from the ground up. This is a hybrid role bridging data science and ML infrastructure—you'll own models end-to-end, from conception through production deployment, monitoring, and retraining. Unlike organizations that split these responsibilities, you'll be equally comfortable authoring models and reasoning about infrastructure, deployment patterns, and observability.
Key responsibilities include:
- Define and evolve ML/DS evaluation, deployment, monitoring, versioning, and retraining practices
- Design evaluation frameworks and harnesses that validate model quality pre- and post-launch
- Build high-impact models for transaction coding suggestions, OCR/document understanding, and RAG/agentic systems
- Make and document ML infrastructure decisions (model registries, feature stores, serving patterns, monitoring) in partnership with data engineering
- Establish technical standards and best practices to scale the ML/DS practice
- Translate business problems into well-scoped modeling questions
- Design and analyze experiments (A/B tests, causal inference) to validate model impact
- Drive adoption of ML infrastructure and tooling improvements
You'll work with Python, Snowflake, DBT, PostgreSQL, Kubernetes, MLflow, Terraform, Prometheus, Google Cloud Services, Omni, Hex, and PowerBI.
Required: 6+ years in data science, applied ML, or quantitative fields with production shipping experience. Deep hands-on understanding of ML infrastructure (registries, feature stores, serving, monitoring, retraining). Strong evaluation framework design experience. Hybrid DS/infrastructure mindset—owning models end-to-end. Track record establishing or maturing ML practices. Strong Python and SQL. Ability to make infrastructure trade-offs with data/platform engineers. Statistical modeling, ML techniques, and experiment design expertise. Excellent communication and stakeholder influence.
Nice-to-haves: RAG systems, vector databases, agents, OCR, document understanding, workflow orchestrators (Airflow, Dagster, Prefect), cloud ML platforms (Vertex AI, SageMaker), Kubernetes, BI tools, fintech/banking background, team mentoring experience.
