About this role
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 problems,buildingandvalidatingprototypes, 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 system‑level perspective, ensuring scalability, maintainability, and long‑term 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 not
blackrock