ROLE SUMMARY

We 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 RESPONSIBILITIES

Solution 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).