Act as a trusted technical advisor to clients, including senior and C-suite stakeholders, across AI Readiness and delivery engagements.
Lead the delivery of AI Readiness Audits, helping clients assess their data, tooling, capability and governance posture.
Own end-to-end project and stakeholder management on engagements, including scoping, planning, timelines, risk and status reporting.
Own solution architecture for ML and Generative AI work, including model selection, tool choice and production readiness.
Design, build and deploy GenAI and ML solutions, including RAG, agentic systems, evaluation pipelines and guardrails alongside the engineering team.
Ensure solutions are production-ready with appropriate LLMOps and MLOps practices: monitoring, evaluation, cost and latency trade-offs.
Translate ambiguous client problems into scoped opportunities and contribute to estimates, proposals and statements of work.
Support the sales motion through discovery sessions, workshops and technical pitches.
Mentor mid and junior data scientists through code reviews, pairing, design reviews and knowledge sharing.
Feed insights, patterns and reusable components from client work back into Xephyr’s GenAI product roadmap.

5 to 10 years in data science, ML or applied AI, with a track record of shipping solutions into production.
Hands-on production GenAI experience: RAG, agents, tool use, evaluations and guardrails.
Fluent with the modern GenAI stack (e.g. LangGraph or equivalent, LlamaIndex, vector DBs such as pgvector/Pinecone/Weaviate, evals frameworks like Ragas, Braintrust or LangSmith).
Solid foundations in classical ML, statistics, experimental design and causal inference.
Strong Python and SQL; comfortable with Git, CI/CD, testing and at least one major cloud AI platform (Snowflake, Bedrock, Vertex, Azure AI Foundry or Databricks).
Working knowledge of modern data platforms, pipelines, warehousing, governance and quality.
Demonstrated success working directly with clients; consulting or professional services background is a strong plus.
Confident running engagements end-to-end, managing scope, timelines and stakeholder expectations.
Clear communicator who can translate complex concepts for both technical teams and senior business stakeholders.
Thrives in fast-moving environments with shifting priorities, evolving toolkits and volatile POCs.
Tertiary qualifications in computer science, engineering, mathematics, statistics, physics or a related quantitative discipline. Postgraduate study in ML, AI or data science is a plus but not required.

Senior Data Scientist

Key skills