New opportunity

AI Instructor, Point72 Academy

Point72 · New York, United States

About this role

The AI Instructor on Point72 Academy’s Investment Professional Development team teaches associates to use AI effectively and responsibly throughout fundamental investment research. The role combines practical instruction, development of AI tools and workflows, and coaching for early-career investment professionals.

Responsibilities

Create lectures, demonstrations, assignments, and stock-based case studies that turn AI concepts into research applications.
Build and maintain tools for hands-on training, research practice, and analyst development, including reusable prompts, templates, Python scripts, document search tools, and workflow automations.
Lead product development from concept to launch, including shared data and analytics workflows associates can deploy and maintain.
Coach associates and troubleshoot APIs, prompts, data preparation, workflow execution, and connections between models and enterprise tools.
Incorporate relevant emerging AI technologies approved for use at the firm into the curriculum. Partner with Compliance on approved data sources, system permissibility, analytics, and AI training initiatives.

Requirements

At least two years of experience in applied AI, data science, technical training, or a related field, plus a bachelor’s degree in computer science, engineering, data science, statistics, or a related discipline.
Strong Python and SQL skills and experience with data-processing technologies such as Spark; experience with AI-assisted development tools such as Claude Code or Codex.
Practical experience building and maintaining LLM applications or agentic workflows connecting models to enterprise data and tools through APIs or Model Context Protocol (MCP). Experience with web scraping, APIs, or other programmatic data collection and ingestion.
Experience teaching, training, mentoring, or explaining technical concepts to non-technical or early-career audiences. Strong communication, organization, and problem-solving skills; interest in financial markets, willingness to learn fundamental investment research, and a commitment to high ethical standards.

The role is listed in New York, New York. Annual base salary is $250,000–$300,000 USD, excluding discretionary bonus and benefits. Listed benefits include fully paid health care, family leave, wellness programs, tuition assistance, and a 401(k) with employer match.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonSQLSparkClaude CodeCodexLarge language model applicationsAgentic workflowsAPIsModel Context Protocol (MCP)Web scrapingData ingestionPrompt developmentDocument searchWorkflow automationAI-assisted developmentTechnical trainingMentoringFundamental investment researchData preparationProduct development

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Teach AI in the context of investment research
The role teaches Academy associates to use AI effectively and responsibly throughout fundamental investment research, using lectures, demonstrations, assignments, and stock-based case studies.
Build tools associates can use
The instructor builds and maintains reusable prompts, templates, Python scripts, document search tools, and automations for hands-on training and research practice.
Own workflows from concept to launch
The posting calls for leading product development, including shared data and analytics workflows that associates can deploy and maintain.
Coach and troubleshoot
The instructor supports early-career associates and resolves issues with APIs, prompts, data preparation, workflow execution, and model connections to enterprise tools.
Keep training within approved use
The role incorporates relevant AI capabilities approved for firm use and partners with Compliance on data sources, system permissibility, analytics, and training initiatives.

Likely challenges to think through

Making technical lessons useful without oversimplifying
Preparation hypothesis: associates may need accessible instruction that still reflects real research tasks. Prepare a stock-based teaching example that distinguishes what an AI workflow can help investigate from what an analyst must verify.
Balancing useful prototypes with maintainable workflows
Preparation hypothesis: a demonstration may be easier to build than a shared workflow associates can deploy and maintain. Prepare to explain how you would test data ingestion, handle API or model failures, and document a reusable workflow.
Adapting to new AI capabilities within permitted boundaries
Preparation hypothesis: emerging tools may offer instructional value before their data sources or systems are approved for use. Prepare an approach for checking permissibility with Compliance before adding a capability to training.

Practice your answers

Technical practice

How would you teach an AI concept through a stock-based research case study?

What to coverSuggested preparation: Describe a lecture, demonstration, and assignment that move from the concept to a fundamental research task. Explain how associates would check AI-generated claims against source material rather than treat the output as a conclusion.

How would you design reusable prompts and document search for associates?

What to coverSuggested preparation: Explain how templates could make a research task repeatable while preserving room for analyst judgment. Address document retrieval, source traceability, and checks for unsupported answers.

How would you use Python, SQL, and Spark in a shared analytics workflow?

What to coverSuggested preparation: Walk through ingestion, cleaning, querying, and processing data for an analyst-facing output. Explain when simpler Python and SQL processing is sufficient and when a technology such as Spark is warranted.

How would you connect an LLM workflow to enterprise data and tools?

What to coverSuggested preparation: Compare an API-based connection with an MCP-based connection in terms of tool access and maintainability. Explain how you would constrain tool use, handle failures, and verify outputs before an associate relies on them.

How would you prepare data collected through APIs or web scraping for research use?

What to coverSuggested preparation: Cover collection, provenance, data quality, and repeatable ingestion. Explain how you would confirm that a source and collection method are permissible before incorporating the data into training or a workflow.

How would you troubleshoot a failing AI research workflow?

What to coverSuggested preparation: Describe how you would isolate problems across the prompt, input data, API, model-to-tool connection, and workflow execution. Explain what you would inspect first and how you would confirm that a fix works.

How would you take a training tool from concept to launch?

What to coverSuggested preparation: Outline how you would define the associate's task, build a usable first version, test it with users, and make the workflow maintainable. Discuss trade-offs between adding capabilities and keeping deployment and upkeep manageable for associates.

How would you use AI-assisted development tools when building a Python training tool?

What to coverSuggested preparation: Explain where a tool such as Claude Code or Codex might speed development. Address how you would review generated code, test behavior, and avoid exposing data that is not approved for the tool.

How would you assess whether an AI use case is permissible for training?

What to coverSuggested preparation: Explain how you would identify the proposed data sources, systems, and analytics and bring specific questions to Compliance. Distinguish technical feasibility from permission to use a capability at the firm.

How would you decide whether an emerging AI capability belongs in the curriculum?

What to coverSuggested preparation: Limit the discussion to technologies approved for use at the firm, as the posting specifies. Describe how you would evaluate relevance to fundamental research, instructional value, reliability, and maintenance effort.

Behavioural practice

Tell me about a time you taught a technical concept to an early-career or non-technical audience.

What to coverSuggested preparation: Use situation, action, and result to show how you adapted the explanation and practice exercise to the audience. Include how you determined whether learners could apply the concept independently.

Tell me about a time you coached someone through a technical workflow problem.

What to coverSuggested preparation: Describe the situation, your diagnostic steps, and the result without presenting the fix as a substitute for learning. Explain how you helped the person handle a similar issue later.

Tell me about a time you worked with a partner to resolve a data-use or system-permission question.

What to coverSuggested preparation: Use a real situation, the actions you took to clarify constraints with the relevant partner, and the outcome. Explain how you adjusted the proposed work when necessary rather than assuming technical access meant approval.

Tell me about a time you led a tool or workflow from idea to use.

What to coverSuggested preparation: Describe the user need, your decisions and coordination from concept through launch, and the result. Include what you learned about usability or ongoing maintenance.

Tell me about a time you faced an ethical concern in a technical project.

What to coverSuggested preparation: State the situation, the action you took, and the result, including any uncertainty you had to resolve. Focus on the judgment and communication involved rather than claiming that a technical safeguard alone settled the issue.

Sharpen your knowledge

Topics to prepare

Responsible AI in investment research
Suggested exercise: outline how you would decide whether a data source, system connection, and AI-assisted analysis are permissible before including them in a lesson. The posting calls for responsible AI use and partnership with Compliance on approved sources and systems; do not assume any particular source is approved.
Fundamental research applications
Suggested exercise: sketch a stock-based case study showing where AI could assist a research task and where an associate would check the result. The role teaches AI throughout fundamental investment research and creates stock-based cases; the posting asks for interest in markets and the ability to learn the research process.
LLM and agentic workflow design
Suggested exercise: diagram a model-to-enterprise-data workflow using an API or Model Context Protocol (MCP), then identify likely points of failure. Practical experience building and maintaining these connections is an explicit requirement.
Teaching and coaching approach
Suggested exercise: plan a short lecture, demonstration, and assignment that move an early-career audience from an AI concept to a research application. The posting explicitly calls for these materials and for coaching associates.
Product lifecycle for shared workflows
Suggested exercise: outline how a shared data-and-analytics workflow would move from concept to launch, including how associates could deploy and maintain it. Leading that lifecycle is a stated responsibility.

Skills to revise

Python, SQL, and Spark data processing
Suggested exercise: use sample data to write a Python cleaning step, a SQL query, and a comparable Spark transformation; check that their outputs agree. Strong Python and SQL skills and experience with technologies such as Spark are explicit requirements.
LLM application integration
Suggested exercise: build a small demonstration that connects a model to a sample data source or tool through an API or MCP, then test an unsuccessful connection. The posting requires practical LLM or agentic workflow experience and mentions troubleshooting these connections.
Programmatic collection and ingestion
Suggested exercise: collect data from a permitted sample API or webpage, parse it into a consistent format, and handle a missing field. The posting explicitly mentions web scraping, APIs, and other programmatic ingestion.
Reusable research training materials
Suggested exercise: create a prompt and template for a sample research task, plus a document-search demonstration, and turn them into an associate assignment. The role develops reusable prompts, templates, document search tools, and hands-on training.
Live explanation and troubleshooting
Suggested exercise: explain a failed prompt or data-preparation step to a non-technical learner, diagnose it aloud, and revise the demonstration. The role coaches early-career associates and resolves prompt, data, and workflow issues.
AI-assisted development review
Suggested exercise: use Claude Code or Codex to draft a short Python script, then inspect, test, and correct the result yourself. Experience with AI-assisted development tools such as these is an explicit requirement.

Your preparation checklist

Map your experience to the requirements
Prepare a factual one-page record of your degree, relevant experience, and examples of teaching or mentoring early-career or non-technical audiences. The posting specifies a related bachelor’s degree and at least two years in a relevant field.
Prepare an LLM workflow walkthrough
Choose a real project, if available, and diagram its data flow, model-to-tool connections through APIs or MCP, maintenance decisions, and failure handling. The role calls for practical LLM application or agentic-workflow experience; use an illustrative design rather than claim work you have not done.
Assemble technical evidence
Select examples that show Python, SQL, data processing such as Spark, programmatic collection or ingestion, and use of an AI-assisted development tool such as Claude Code or Codex. Be ready to explain your own contribution and how you checked the results.
Draft a short investment-research lesson
Outline a lecture, demonstration, assignment, and stock-based case study showing how associates could use AI responsibly in fundamental research. This is a preparation exercise based on the advertised teaching duties, not a stated interview assignment.
Sketch a reusable training tool
Prepare a small design for a prompt, Python script, document-search tool, or workflow automation that associates could deploy and maintain. Describe its inputs, outputs, documentation, and path from prototype to launch.
Rehearse a coaching scenario
Practice explaining a technical issue to an early-career associate, then troubleshooting a prompt, API connection, data-preparation step, or workflow execution problem. Show how you would diagnose it without assuming access to Point72 systems.
Prepare governance and team questions
Write questions about approved data sources, permissible systems, Compliance collaboration, and how emerging AI capabilities enter the curriculum. Prepare to discuss safeguards you have used or would propose, distinguishing your suggestions from firm policy.

Availability last checked: Nov 4, 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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