Senior Applied AI Researcher, Vice President

We are looking for a Senior Applied AI Researcher (6+ years of experience) to join our data science team working on advanced AI-driven solutions. This is primarily an individual contributor role, with responsibility for owning end-to-end ownership of complex modelling / AI problem areas and technical ownership of AI capabilities critical to the product.

The role focuses on the research, prototyping, evaluation, and improvement of AI solutions, with hands-on work across LLM-based systems, including agent-style workflows and retrieval-augmented generation (RAG) where appropriate. You will work end-to-end: collaborating with stakeholders and product managers to define problems, building and validating prototypes, 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 from problem definition through prototype validation and production support.

• Partner with product managers and business stakeholders to 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 prototype LLM-powered solutions, including RAG-based systems and 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 and maintain evaluation frameworks to assess AI system performance (accuracy, reliability, relevance, robustness, safety).

• Develop quantitative and qualitative metrics, benchmarks, and testing approaches to validate prototypes and track improvements.

• Analyze existing solutions to identify gaps and drive continuous, data-driven performance enhancements.

Collaboration and communication

• Work closely with data scientists, engineers, and product teams to ensure smooth transition from prototype to production.

• Clearly communicate methods, assumptions, results, and limitations to technical 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 an Agile / SCRUM development environment.

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

Required Qualifications

• 6+ years of 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.

• Strong Python skills for analysis, modelling, experimentation, and prototyping.

• Strong SQL skills for data exploration, transformation, and analytical workflows.

• Excellent analytical thinking and problem-structuring abilities; comfort operating independently with loosely defined goals.

• Experience using Git for version control and collaborative development.

• Strong English communication skills, both written and verbal.

Preferred Qualifications (Strong Plus)

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

• Experience with RAG systems, including retrieval strategies, chunking, evaluation, and performance tuning.

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

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

• Understanding of software engineering best practices, including testing strategies and CI/CD concepts.

• Experience working in Azure or similar cloud environments.

• Familiarity with Snowflake (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 required for strong technical candidates.

What Success Looks Like in This Role

• You independently deliver high-quality AI prototypes and 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 confidently productionize your work thanks to clear designs, metrics, and collaboration.

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