Founding Senior Data Scientist/ML
AI summary
BrighterMonday Uganda is hiring a Founding Senior Data Scientist to shape the company's ML and data science practices end to end. The role combines modeling and ML infrastructure work, covering evaluation frameworks, deployment, monitoring, and high-impact products such as transaction coding suggestions, OCR/document understanding, and RAG/agentic systems. Candidates should have strong Python and SQL skills plus hands-on experience shipping models into production.
- Founding-level role with wide latitude to set ML standards and infrastructure
- Hybrid data science and ML infrastructure ownership, not just modeling
- Work on transaction coding, OCR, RAG and agentic systems
- Requires 6+ years of applied data science or ML experience
- Strong Python, SQL, and production ML infrastructure skills needed
AI job guide
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AI salary guide
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Can you qualify for this role?
- Required6+ years of relevant experienceThe job post includes a minimum experience signal.
- PreferredPractical evidence in senior, founding, individual contributorThe tags and summary point to skills connected with this role.
- RequiredAvailability to work in Not specifiedThe vacancy is associated with this location.
Documents to prepare
- Likely requiredUpdated CV
- Role specificCover letter or short employer message
- OptionalProfessional references
- VerifyID or passport only after verifying the employer
Application tips for this job
- Place your strongest Founding Senior Data Scientist/ML evidence in the first half of your CV.
- In your cover letter or employer message, connect your experience to BrighterMonday Uganda and the role in Not specified.
- Add concrete examples related to senior, founding, individual contributor, ideally with measurable outcomes or clear responsibilities.
- Follow the instructions from BrighterMonday Uganda; avoid sending documents to unofficial contacts or copied links.
- Confirm the deadline, interview location and employer contact before sharing personal documents.
- Prepare a polite question about pay, benefits and contract terms for later interview stages.
Source and safety check
- BrighterMonday Uganda
- Original source link available
- Application method is clear
- Deadline not specified
- No major risk signal was detected in the captured text.
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Interview preparation
- What experience makes you a strong fit for this Founding Senior Data Scientist/ML role in senior, founding?
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- Are you available to work in Not specified under the listed contract or schedule?
- Prepare examples with clear responsibilities, tools used and measurable outcomes.
- Review the source and research BrighterMonday Uganda before the interview.
Ask what the first priorities will be in the role and how success will be measured.
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Original source description
Our team is looking for a Founding 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
- 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
- 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
- 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
- with RAG (Retrieval-Augmented Generation) systems, vector databases, or building/deploying agents
- with OCR, document understanding, or other unstructured data extraction problems
- Familiarity with workflow orchestrators such as Airflow, Dagster, or Prefect
- with cloud ML platforms (GCP Vertex AI, AWS SageMaker, or similar)
- Comfort with containerization and Kubernetes for model deployment
- with BI tools such as Omni or Power BI
- Background in fintech, banking, or financial services data
- 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)
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