AI Strategist

What matters most

• Set a firm-wide AI direction

  The role calls for developing an approach across teams and prioritizing opportunities to improve workflows, decision-making and execution. Prepare to explain how you would compare use cases and turn the strongest ones into a coherent plan.

• Deliver measurable projects that can scale

  The posting specifies project objectives, owners, workflows, milestones and measurement methods, plus a path from experiments to scalable use. Prepare an example of how you would define success and decide whether to expand an AI initiative.

• Make practical model and implementation choices

  The role advises on model selection, token optimization and resource use, and may involve code, automations, integrations or prototypes. Deep familiarity with Claude for research, analysis and project execution is specifically requested. Prepare to explain the reasoning behind technical choices, not just the tools used.

• Enable adoption across teams

  The posting calls for understanding stakeholder needs, training users and documenting standards, playbooks and lessons learned. Prepare to describe how you would make a useful AI workflow understandable and reusable beyond its initial users.

Likely challenges to think through

• Choosing among competing opportunities

  A likely difficulty, not a stated employer problem, is balancing requests from multiple functions against limited implementation capacity. Prepare a prioritization approach that weighs expected impact, feasibility and the ability to measure results.

• Moving beyond a successful prototype

  The explicit emphasis on scalable use suggests a possible trade-off between rapid experimentation and reliable, repeatable workflows. Prepare to explain what you would validate, document and assign ownership of before broader rollout.

• Balancing AI quality and resource use

  Model selection and token optimization can require trade-offs among output quality, context needs and efficiency. Prepare a concrete way to compare options for a defined task and recognize when a cheaper or shorter approach is insufficient.

Topics to prepare

• Firm-wide AI opportunity prioritization

  The role calls for identifying and ranking opportunities across teams. As a preparation exercise, rank three hypothetical workflow improvements by expected impact, feasibility, resource use and how results could be measured; explain the trade-offs.

• AI project design and measurement

  The posting specifies objectives, owners, workflows, milestones and measurement methods. Revise how these fit together by sketching a project charter for one hypothetical AI initiative and defining a baseline and success measure.

• Prompting, context and Claude capabilities

  The qualifications name prompt engineering, context management, model capabilities and deep familiarity with Claude for research, analysis and project execution. Outline how you would structure a research task, supply relevant context and check the output before use.

• Model selection and efficient resource use

  The role advises on model selection and token optimization. Prepare a decision framework comparing candidate approaches by task fit, context needs, output quality and resource use; describe when a shorter input would or would not be appropriate.

• From experiment to scalable adoption

  The posting calls for implementation methods, reusable standards, user training and adoption support. Map the conceptual stages from prototype to wider use, including evaluation, ownership, documentation and user feedback.

Skills to revise

• Run a Claude workflow

  Practice using Claude to research and analyze a hypothetical business question, then turn the findings into an action plan. Record the inputs, output checks and revisions; this reflects the stated expectation of strategic, hands-on Claude use.

• Build an AI project plan

  Draft a one-page plan with an objective, named role owners, workflow, milestones and measures. Practice explaining it to stakeholders, since the role organizes projects across teams.

• Prototype a workflow integration

  As a preparation exercise, build a small AI-enabled prototype using scripting or an API with sample data, and demonstrate its input, output and failure handling. The posting asks for coding or automation experience and familiarity with APIs and data workflows, but names no required language or platform.

• Practice stakeholder discovery and training

  Role-play a discussion to identify a team's workflow need, then prepare a short user walkthrough and collect feedback. This mirrors the stated responsibilities for stakeholder partnership, training and adoption.

• Write a reusable AI playbook

  Create a brief guide for the hypothetical prototype covering setup, suitable uses, output checks and lessons learned. The role explicitly calls for documenting standards and playbooks for reuse.

• Draft an AI opportunity shortlist

  The role calls for firm-wide prioritization. Prepare a one-page shortlist of possible workflow, decision-making, and execution improvements for a specialty finance investment manager; label the use cases as hypotheses and explain how you would rank them.

• Prepare a Claude project walkthrough

  The posting emphasizes hands-on use of Claude for research, analysis, and project execution. Bring a professional example, if available, showing the objective, prompts and context, your review of outputs, and the result. Use a hypothetical example if you cannot share past work.

• Sketch a project delivery plan

  For one shortlisted use case, draft a sample plan with an objective, proposed owner, workflow, milestones, and ways to measure results. The posting names these as elements of AI project organization; the plan is your proposed approach, not a company process.

• Explain model and resource choices

  Prepare a short comparison showing how you would choose a model, manage context and prompts, and reduce token use for the sample project. State what you would test rather than assuming a particular tool or configuration is in place.

• Show a technical implementation example

  Select code, an automation, an integration, or an AI-enabled prototype you can discuss. Be ready to explain its API, scripting, or data-workflow components, what you built yourself, and how you checked that it worked.

• Outline a path from pilot to adoption

  Draft a sample plan for moving an experiment into wider use, including stakeholder feedback, user training, a reusable playbook, and lessons learned. These activities reflect the posting; your sequence is a preparation suggestion.

• Write questions about the operating context

  Prepare questions about the investment manager’s highest-priority workflows, existing AI project environments, how success is measured, and how stakeholders and technical teams work together. These details are not specified in the posting.