ROLE SUMMARYWe are looking for a Senior Data Scientist to lead complex data science engagements that combine traditional statistical modelling with Generative AI. You will work hands-on with very large datasets across disparate systems and formats, translate ambiguous business problems into rigorous analytical solutions, and present those solutions clearly to C-level stakeholders. This is a delivery-first role with a fast track into technical leadership: alongside your own project work, you will help guide junior data scientists and shape how Lynx builds and ships data science solutions.
KEY RESPONSIBILITIESSolution Design & Delivery- Design and deliver end-to-end solutions for defined data science problems, combining classical modelling, data transformation, and Generative AI / LLM techniques.
- Work hands-on with very large datasets across disparate stores and formats, from ingestion and transformation through to modelling and validation.
- Apply statistical and machine learning methods to business problems such as customer retention, campaign management, and commercial performance optimisation.
Client Communication & Leadership- Present results and prepare client-ready materials for project stakeholders, including C-level audiences, translating technical work into clear business narratives.
- Lead smaller data science workstreams, with support from internal leadership and the PMO, including day-to-day guidance for junior team members.
- Partner with delivery and account teams to scope problems, set realistic timelines, and manage stakeholder expectations.
Knowledge Building- Create reusable documentation, presentations, and code libraries during projects so future engagements can build on prior work.
- Participate in internal education, research, and knowledge-sharing initiatives that raise the technical bar across the practice.
SKILLS, QUALIFICATIONS AND EXPERIENCE- 8+ years of overall experience in data science, with a track record of leading analytical workstreams independently.
- Degree in Mathematics, Statistics, Economics, Computer Science, Engineering, or a related field; MSc or PhD preferred.
- Solid grounding in probability theory, statistics, and core data science algorithms, with applied experience in areas such as customer retention and campaign management.
- Strong hands-on proficiency in Python for data analysis, modelling (PyTorch, TensorFlow, or JAX), and productionising code.
- Strong SQL, and comfort working across common data stores (relational, columnar/warehouse, and vector databases).
- Git and GitHub proficiency, including branching workflows and code review; experience with GitHub Actions (or equivalent CI/CD) preferred.
- Hands-on experience designing and building agentic LLM applications - tool calling, multi-step orchestration, and state management - using at least one modern framework (e.g., LangGraph, Pydantic AI, AWS Bedrock AgentCore, Google ADK, or the OpenAI Agents SDK), beyond simple prompt-and-response use of LLM APIs.
- Preferred: practical depth in one or more of MCP-based tool integration, RAG and embedding pipelines (including vector stores), model fine-tuning and RL-based post-training, and LLM guardrails and evaluation (e.g., Ragas, DeepEval, Langfuse, or similar).
- Experience with at least one major cloud platform (AWS, GCP, or Azure); Docker and basic containerised deployment preferred.
- Comfortable working with very large, complex datasets residing in different data stores and formats.
- Excellent verbal and written communication skills, with strong data visualisation ability and experience presenting to senior, non-technical stakeholders.
- Demonstrated leadership potential and the presence to guide junior team members and represent the company with clients.
- Nice to have:
- Software engineering hygiene (preferred): typed Python (Pydantic), testing with pytest, packaging, and dependency management (uv).
- Experience shipping LLM applications to production, including observability and cost/latency management (e.g., Langfuse, Phoenix, or similar LLMOps tooling).
- Experience in the life sciences industry is preferred.
KEY COMPETENCIES- Executive Communication: Translates complex Data Science solutions into plain language for C-level and non-technical stakeholders.
- Technical Depth: Brings rigorous statistical and modelling judgement, paired with fluency in modern GenAI/LLM approaches.
- Discretion & Integrity: Handles sensitive client and internal information with professionalism and sound judgement.
- Leadership & Charisma: Guides junior colleagues day to day, even without a formal management title, and takes pride in their growth.
- Collaboration: A team player who builds strong working relationships across delivery teams, PMO, and clients.
