About Us
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.
About the Role
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
Responsibilities:
Help define and evolve Rho's ML/DS practices: how models get evaluated, deployed, monitored, versioned, and retrained
Design and implement evaluation frameworks and eval harnesses that give the company real confidence in model quality, before and after launch
Build and own high-impact models and analyses powering products like transaction coding suggestions, OCR/document understanding, and RAG/agentic systems
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
Set technical standards and best practices that help the ML/DS practice scale as the team grows
Translate ambiguous business problems into well-scoped modeling questions
Design and analyze experiments (A/B tests, causal inference) to validate model impact rigorously
Advocate for and drive adoption of ML infrastructure and tooling improvements as needs grow
Requirements:
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
Nice To Haves:
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
What we offer
Our people are our most valuable asset. Base salary may vary depending on relevant experience, skills, geographic location, and business needs.
Benefits:
Top-notch Private Healthcare Insurance for you and your family members
Generous PTO policy
Lunch at work
Covered costs for parking for onsite staff
Learning and development budget
Paternity leave
Hybrid work environment (with old town Belgrade office)
Skills Required
- 6+ years of experience in data science, applied machine learning, or a related quantitative field
- Track record of shipping machine learning models into production
- Deep hands-on understanding of ML infrastructure, including model registries, feature stores, serving architectures, model monitoring, observability, and retraining pipelines
- Experience designing evaluation frameworks and evaluations for machine learning systems, including offline metrics and ongoing production evaluation
- Ability to operate as a hybrid data science and ML infrastructure practitioner and own models end to end
- Experience establishing or maturing ML practices, standards, or infrastructure within a team or company
- Strong programming skills in Python and solid SQL skills
- Ability to make infrastructure trade-off decisions with data and platform engineers and communicate across modeling and systems disciplines
- Experience with statistical modeling, machine learning techniques, and experiment design
- Excellent communication and stakeholder-influence skills
- Experience with RAG systems, vector databases, or building and deploying agents
- Experience with OCR, document understanding, or unstructured data extraction
- Familiarity with Airflow, Dagster, or Prefect
- Experience with cloud ML platforms such as GCP Vertex AI or AWS SageMaker
- Experience 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 data science or ML team
Rho Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Rho and has not been reviewed or approved by Rho.
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Fair & Transparent Compensation — Pay is characterized as market-competitive across key product, engineering, and sales roles, and compensation is highlighted as a relative strength in employer profiles. Compensation satisfaction appears strong compared to other categories, even when other aspects of the experience are mixed.
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Healthcare Strength — Core medical, dental, and vision coverage plus life and disability insurance and HSA/FSA options are consistently listed across postings. This breadth of standard healthcare benefits positions the package as competitive for a growth-stage fintech.
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Leave & Time Off Breadth — PTO, paid holidays, parental leave, and bereavement leave are explicitly included in third-party listings. Hybrid flexibility is also noted, supporting practical use of time off.
Rho Insights
What We Do
Rho's mission is to make finance frictionless for organizations. Our automated, integrated platform has everything a finance leader needs across commercial banking and spend management to save finance leaders money and boost their team’s productivity.
Why Work With Us
Rho is solving a significant challenge for finance leaders. This is a great opportunity to be part of a special mission to make finance frictionless. Rho is perfect for high-performers who move fast, know how to prioritize for impact, lead with empathy, and bring diverse skillsets and perspectives to help make customer lives easier.
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