Senior Data Scientist (GenAI Solutions) - Hybrid

What matters most

• Production-ready employee assistants

  XPO calls for building, deploying and scaling GenAI chatbots and assistants that help employees find information and complete business tasks. For preparation, be ready to explain how an assistant moves from a useful prototype to a dependable deployed service.

• Secure enterprise RAG

  The role develops RAG applications connecting LLMs to enterprise documents, databases and internal knowledge sources securely. Prepare to discuss retrieval design, source relevance and how access to internal information would be controlled.

• Agentic workflow integration

  The role builds multi-step workflows using tools, APIs and enterprise systems, and integrates GenAI with internal applications and data platforms. Prepare a concrete approach to tool selection, workflow boundaries and handling failed steps.

• Quality, guardrails and monitoring

  XPO expects improvements through prompts, retrieval strategies, evaluation and response optimization, alongside guardrails, monitoring and governance. Prepare to explain how you would measure response quality and detect unreliable or unsafe behavior after deployment.

• Cross-functional delivery and business value

  The role partners with Engineering, Product, Cybersecurity and business teams from proof of concept through production, while identifying opportunities to improve productivity and operational efficiency. Prepare to translate technical choices into clear business recommendations.

Likely challenges to think through

• Useful answers versus restricted information

  A likely challenge is making enterprise retrieval broad enough to answer employee questions without exposing information beyond a user's access. Prepare to describe how you would test answer usefulness and enforce access boundaries together; this is a preparation hypothesis, not a reported XPO issue.

• Automation versus control

  Multi-step agents may save effort but can make incorrect tool calls or take unintended actions. Prepare to discuss when an agent should act, ask for confirmation or stop, and how its actions could be evaluated and monitored. This is an inferred trade-off, not a confirmed employer problem.

• Prototype speed versus production reliability

  Moving assistants from proof of concept to deployment may require balancing rapid experimentation with integration, security and operational reliability across teams. Prepare an example of how you would define release criteria and resolve cross-functional trade-offs; this difficulty is inferred from the stated duties.

Topics to prepare

• Secure enterprise RAG design

  Sketch how an assistant would retrieve from documents, databases and internal knowledge while respecting access boundaries. The posting explicitly calls for secure RAG applications connected to these sources.

• Agentic workflow design

  Diagram a multi-step business task, marking where an agent calls a tool or API, uses an enterprise system and handles a failed step. The role explicitly involves agentic workflow automation.

• Conversational AI evaluation

  Define how you would compare two retrieval and prompt approaches for answer accuracy and usefulness. The posting names prompt engineering, retrieval strategies, model evaluation and response optimization.

• Guardrails, monitoring and governance

  List plausible unsafe or inaccurate assistant responses and the controls or monitoring signals you would use to detect them. The role includes helping make solutions accurate, secure, scalable and reliable.

• Machine learning and deployment fundamentals

  Review when supervised versus unsupervised learning is appropriate, then outline development, testing, evaluation and deployment stages. Familiarity with these learning approaches is a minimum qualification; the broader lifecycle is preferred.

Skills to revise

• Apply a listed programming language

  As practice, implement and test a small AI or ML workflow in a language named in the posting, such as Python. Practical programming in at least one listed language is a minimum qualification; Python AI/ML libraries are preferred.

• Build a small RAG assistant

  As practice, connect an assistant to sample documents and a sample database, retrieve relevant content and check that answers stay within what the retrieved content supports. This mirrors the stated RAG responsibility; the sample sources are a preparation suggestion.

• Implement a multi-step tool workflow

  As practice, create a mock API and have an agent use it across two steps, including a failed-call path. Tool calling, API integration and workflow orchestration appear in the responsibilities and preferred qualifications.

• Run a response-improvement test

  As practice, write a short set of test questions, compare prompt or retrieval changes, and record answer errors before and after. The posting explicitly calls for model evaluation and conversational response optimization.

• Exercise guardrails and monitoring

  As practice, test an assistant against an unauthorized-information request and an unsupported-answer request; record how each is handled and what would be monitored. This prepares for the stated guardrails, monitoring and governance work.

• Explain a production recommendation

  As practice, give a brief business-facing recommendation for taking a GenAI proof of concept to production, including an evaluation result and a security concern. Clear business communication is a minimum qualification, and the role partners with Engineering, Product, Cybersecurity and business teams.

• Map the stated qualifications to evidence

  Prepare a one-page matrix with examples of GenAI or applied AI work, practical use of at least one listed language in an AI or ML project, supervised and unsupervised learning, and communication of AI concepts as business recommendations. Distinguish your experience from the role’s minimum and preferred qualifications.

• Prepare a GenAI project walkthrough

  If you have a relevant chatbot, assistant, or agent project, outline the business task, your contribution, architecture, deployment decisions, results, and tradeoffs. If not, use a clearly labeled hypothetical design rather than implying production experience.

• Sketch a secure enterprise RAG design

  Draw a proposed assistant that retrieves from documents, databases, or internal knowledge sources. Be ready to explain access controls, retrieval choices, source attribution, and how you would test answer quality; these are preparation suggestions based on the stated RAG responsibility.

• Rehearse an agentic workflow design

  Diagram a hypothetical multi-step business task using tool calls, APIs, and enterprise systems. Specify where the workflow checks inputs, handles failed calls, and requires human review rather than assuming unrestricted automation.

• Build an evaluation and guardrails plan

  Prepare a small test matrix covering retrieval and response quality, prompt changes, security, reliability, and monitoring. Explain what evidence would justify moving a proof of concept toward production; the posting names evaluation, guardrails, and governance but does not prescribe particular metrics.

• Practice a cross-functional production discussion

  Prepare a concise example, if available, of working with Engineering, Product, Cybersecurity, and business stakeholders. Show how you translated a technical choice into a business recommendation and addressed deployment or integration constraints.

• Draft targeted questions for XPO

  Ask which employee information-access or task-completion problems are highest priority, what enterprise sources and systems an assistant would connect to, and how teams assess success, security, and production readiness.

• Plan for the stated interview format

  Prepare a web-interview setup without a virtual background or headphones or earbuds, and plan for at least one onsite, in-person interview in Boston, as the posting specifies.