Senior Modelling Data Scientist, Vice President

We are looking for aSenior Data Scientist (6 years of experience)to join our data science team working on advancedAI-driven solutions. This is primarily anindividual contributor role with responsibility for owningend-to-end ownership of complex modeling / AI problem areasand technical ownership of AI capabilities critical to the product.

The role focuses on theresearch prototyping evaluation and improvement of AI solutions with hands-on work acrossLLM-based systems includingagent-style workflows and retrieval-augmented generation (RAG)whereappropriate. You will work end-to-end: collaborating with stakeholders and product managers to define problemsbuildingandvalidatingprototypes presenting findings to diverse audiences and supporting engineering teams during implementation and production rollout.

Key Responsibilities

End-to-end AI solution ownership

• Own AI projects or functional modules fromproblem definition through prototype validation and production support.

• Partner withproduct managers and business stakeholdersto translate real-world problems into clearly scoped data science and AI initiatives.

• Independently plan and execute research experimentation and iteration cycles in ambiguous problem spaces.

• Design AI solutions with a systemlevel perspective ensuring scalability maintainability and longterm sustainability.

Applied AI LLMs and agentic systems

• Design and prototypeLLM-powered solutions includingRAG-based systemsand agent-like workflows (e.g.tool use orchestration multi-step reasoning).

• Contribute to defining system behavior scope and constraints with attention to quality robustness and operational considerations.

• Stay current with emerging AI techniques and apply them pragmatically to solve business problems.

Evaluation validation and performance improvement

• Build andmaintainevaluation frameworksto assess AI system performance (accuracy reliability relevance robustness safety).

• Develop quantitative and qualitative metrics benchmarks and testing approaches tovalidateprototypes and track improvements.

• Analyze existing solutions toidentifygaps and drive continuous data-driven performance enhancements.

Collaboration and communication

• Work closely withdata scientists engineers and product teamsto ensure smooth transition from prototype to production.

• Clearly communicate methods assumptions results and limitations totechnical and non-technical audiences.

• Support engineering teams during implementation by clarifying evaluation criteria edge cases and expected system behavior.

• Serve as a technical authority and actively mentor junior data scientists shaping best practices in experimentation evaluation and AI system design.

Ways of working

• Contribute actively within anAgile / SCRUMdevelopment environment.

• Apply good engineering hygiene in research and prototype code to enable reproducibility and collaboration.

Required Qualifications

• 6 yearsof experience in data science applied machine learning or a closely related role.

• Strong mathematical statistical and machine learning foundations including probability statistics optimization and model evaluation.

• Proven ability to select apply and critically evaluate ML models and algorithms for real-world problems.

• StrongPythonskills for analysis modeling experimentation and prototyping.

• StrongSQLskills for data exploration transformation and analytical workflows.

• Excellent analytical thinking and problem-structuring abilities; comfortoperatingindependently with loosely defined goals.

• Experience usingGitfor version control and collaborative development.

• StrongEnglish communication skills both written and verbal.

Preferred Qualifications (Strong Plus)

• Hands-on experience withLLMs including prompt/system design and building real-world applications.

• Experience withRAG systems including retrieval strategies chunking evaluation and performance tuning.

• Experience designing or contributing toagent-style AI systemsand familiarity with concepts such as agent evaluation guardrails and reliability testing.

• ML modeling experience (e.g.supervised learning ranking classification) beyond exploratory analysis.

• Understanding ofsoftware engineering best practices including testing strategies and CI/CD concepts.

• Experience working inAzureor similar cloud environments.

• Familiarity withSnowflake(and optionally Snowflake AI) as part of a modern data stack.

• Experience collaborating closely with domain experts;financial domain exposure is a plus but notfor strong technical candidates.

What Success Looks Like in This Role

• You independently deliverhigh-quality AI prototypesand evaluation frameworks for a defined subdomain or application module.

• You proactively define scope success metrics and experimentation plans helping shape architecture and design decisions.

• Your evaluation approaches enable reliable comparison regression prevention and continuous improvement of AI solutions.

• Engineering teams can confidentlyproductionizeyour work thanks to clear designs metrics and collaboration.

• Over time you raise the technical maturity of AI development and evaluation practices within the team.