Senior Modelling Data Scientist, Vice President

About this role We are looking for a Senior Data Scientist ( 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 modeling / AI problem areas and t echnical 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, modeling, 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 system ... (truncated, view full listing at source)