Senior Data Scientist analyzing data to drive customer success and business growth at Adobe. Conducting statistical analyses and collaborating with multi - functional teams for product enhancement.
About the role
- Senior Data Scientist in a hybrid role focusing on AI Readiness and solutions delivery for clients. Mentoring junior data scientists while managing client engagements and project execution.
Responsibilities
- 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.
Requirements
- 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.
Benefits
- Professional development opportunities
- Flexible work arrangements
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