New opportunity

AI Product Management

Royal Bank of Canada · New York, United States of America

About this role

RBC Capital Markets’ AI & Digital Innovation team seeks an AI Product Manager to develop desk-facing AI and data products for Sales & Trading. The role focuses on helping sales teams understand clients, supporting how sales and traders set prices, and improving how pricing drives sales. This is a full-time, 40-hour-per-week position in New York, New York, United States of America. Building models is not part of the role.

Responsibilities

Identify and prioritize applied AI opportunities; turn ambiguous commercial requests into scoped, testable product definitions with clear success criteria.
Own the product roadmap and sequencing, document trade-offs, and align business and delivery partners on scope, expectations, and acceptance criteria.
Connect desk users with engineering, data science, and quantitative partners; evaluate whether delivered capabilities work for their intended users.
Drive adoption and use post-release feedback to improve usability, reliability, and practical value.
Guide initiatives through risk, compliance, and AI governance processes, addressing controls, documentation, and human oversight before release.

Required qualifications

5+ years delivering AI, analytics, or data products for professional users in capital markets or a similarly regulated setting, with end-to-end ownership from problem definition through evaluation and adoption.
Experience with product discovery, roadmap ownership, prioritization, acceptance criteria, and adoption measurement across competing stakeholder requests.
Fluency in applied AI and Generative AI, including evaluation, reliability, and limitations; quantitative comfort with pricing, trading, and risk; and an understanding of capital markets Sales & Trading workflows.
Strong written and verbal communication with senior stakeholders and the ability to present options, trade-offs, and recommendations concisely.

Preferred qualifications

A post-secondary degree in STEM, Quantitative Finance, Economics, or a related quantitative field, or equivalent hands-on technical experience.
Experience with AI or data products in regulated or security-conscious environments, including model risk, compliance, or AI governance; exposure to electronic trading, automated pricing, predictive analytics, market microstructure, or quantitative finance.

The expected salary range is $120,000 to $225,000, dependent on experience, skills, registration status, market conditions, and business needs. Benefits include a company-matched 401(k), health, dental, vision, life and disability insurance, and paid time off. Discretionary variable compensation may be available subject to business and individual performance. The stated application deadline is October 16, 2026; applications are accepted until 11:59 PM the preceding day.

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

Skills for this role

AI Product ManagementProduct DiscoveryProduct Roadmap DevelopmentBusiness AnalysisStakeholder ManagementGenerative AIApplied AIMachine LearningAI EvaluationAI GovernanceRisk ManagementDerivatives PricingMathematical FinancePredictive AnalyticsQuantitative TradingSales & Trading WorkflowsAcceptance CriteriaAdoption MeasurementCommunication

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

Define valuable desk-facing opportunities
Identify applied AI opportunities across client understanding, price-setting, and how pricing drives sales. Turn ambiguous Sales & Trading requests into scoped, testable product definitions with clear success criteria.
Own roadmap decisions
Sequence initiatives across competing business and technology requests, document trade-offs, and align stakeholders and delivery partners on scope and expectations.
Verify capabilities work for desk users
Bridge Sales & Trading users with engineering, data science, and quantitative partners. Agree on problem definitions and acceptance criteria, then evaluate whether delivered capabilities work for their intended users. The role does not involve building models.
Make adoption measurable
Drive use after release and draw on observed needs and user feedback to improve usability, reliability, and practical value.
Build governance into delivery
Carry initiatives through risk, compliance, and AI governance, identifying control, documentation, and human-oversight expectations early and meeting them before users receive a capability.

Likely challenges to think through

Choosing among competing desk requests
Likely preparation challenge, not a reported RBC problem: requests may differ in commercial value, feasibility, and delivery effort. Prepare an example of making a sequencing recommendation with explicit criteria, a documented trade-off, and a concise explanation for senior stakeholders.
Evaluating AI in pricing-related workflows
Likely preparation challenge, not a confirmed product issue: a capability can appear useful while being unreliable or poorly suited to a trading decision. Prepare to explain how you would agree on user-facing acceptance criteria with desk and quantitative partners, test limitations, and distinguish product evaluation from model building.
Balancing usable delivery with oversight
Likely preparation challenge, not evidence of a governance bottleneck: controls and human oversight must be met before release without losing the capability’s practical value for desk users. Prepare an approach that surfaces review needs early, defines what a user must verify or control, and incorporates post-release feedback.

Practice your answers

Technical practice

How would you turn an ambiguous Sales & Trading request into a testable AI product definition?

What to coverThe role calls for translating commercial questions into scoped definitions with clear success criteria. Suggested approach: identify the desk user, the decision to support, what is in scope, and the evidence that would show the capability is useful.

How would you identify a useful AI opportunity for helping sales understand clients?

What to coverClient understanding is one of the posting’s stated focus areas. Suggested approach: start with a specific user need and compare the proposed capability with simpler ways to meet it, considering usefulness and reliability.

What would you need to understand before proposing a product that supports pricing decisions?

What to coverThe posting requires fluency in Sales & Trading workflows and quantitative comfort with pricing, trading, and risk. Suggested approach: explain whose decision the product supports, what context quantitative partners must validate, and where human judgment remains important.

How would you evaluate whether pricing support improves sales outcomes?

What to coverThe role spans how a price is set and how that price drives a sale. Suggested approach: distinguish evidence that users can make a better-informed pricing decision from evidence of a commercial outcome, and discuss factors outside the product’s control.

How would you sequence competing desk-facing AI initiatives on a roadmap?

What to coverRoadmap ownership, explicit prioritization, and documented trade-offs are stated responsibilities. Suggested approach: compare user value, feasibility, dependencies, and risk, then explain what you would defer and why.

What acceptance criteria would you agree with desk and technical partners before release?

What to coverThe product manager is expected to align desk users with engineering, data science, and quantitative partners and establish evidence that a capability works for its users. Suggested approach: cover intended use, observable task outcomes, reliability, and conditions under which users should not rely on the output.

How would you assess the limitations of a Generative AI capability for desk users?

What to coverApplied AI and Generative AI evaluation, reliability, and known limitations are required; building models is not part of this role. Suggested approach: discuss representative user tasks, incorrect or unsupported outputs, and the trade-off between utility and the need for review.

How would you measure adoption and decide what to improve after release?

What to coverThe posting calls for adoption measurement and learning from observed needs, usability, and reliability. Suggested approach: distinguish usage from practical value and explain how user feedback and observed failures would inform the next product decision.

How would you prepare an AI capability for risk, compliance, and governance review?

What to coverThe role requires controls, documentation, and human oversight expectations to be understood early and met before users receive a capability. Suggested approach: explain how you would surface intended use and limitations, agree on oversight with relevant partners, and resolve release-blocking concerns.

What product trade-offs might change in an automated-pricing or electronic-trading context?

What to coverExposure to automated pricing and electronic trading is listed as nice-to-have, not a requirement. Suggested approach: if relevant to your experience, discuss how speed, reliability, risk, and human oversight could affect scope and evaluation without assuming a particular RBC implementation.

Behavioural practice

Tell me about a time you resolved competing business and technology priorities.

What to coverThe role requires alignment on scope and sequencing across stakeholder requests. Suggested approach: use a real situation, describe the trade-offs you made explicit and the action you took, then state the result.

Describe a time you bridged a gap between professional users and technical partners.

What to coverThe posting emphasizes connecting desk users with engineering, data science, and quantitative partners. Suggested approach: explain the disagreement or misunderstanding, how you established a shared problem definition or acceptance criteria, and what resulted.

Tell me about a time user feedback changed a product after release.

What to coverPost-release adoption, usability, and reliability are part of the stated work. Suggested approach: give the situation, the feedback or observation you acted on, the change you made, and evidence of its result.

Describe a time a risk or compliance concern changed a planned launch.

What to coverThe role carries initiatives through risk, compliance, and AI governance before release. Suggested approach: explain the concern, how you worked with the relevant partners and adjusted the plan, and the eventual outcome.

Tell me about a time you presented a difficult product recommendation to senior stakeholders.

What to coverConcise communication of options, trade-offs, and recommendations is a stated qualification. Suggested approach: describe the decision and its stakes, how you framed the alternatives, and what happened after your recommendation.

Sharpen your knowledge

Topics to prepare

Sales & Trading workflows
Posting: The products are desk-facing and span how sales understand clients, how sales and traders set prices, and how prices drive sales. Suggested revision exercise: Sketch that workflow and identify where a product decision would need input from sales, trading, and delivery partners.
Pricing, trading, and risk concepts
Posting: Quantitative comfort in these areas is required for working with quantitative stakeholders; market microstructure and quantitative finance are nice-to-haves. Suggested revision exercise: Explain, in plain language, what pricing and risk questions you would clarify before scoping a desk-facing capability.
Applied AI and Generative AI limitations
Posting: The role requires fluency in evaluation, reliability, and known limitations, but does not involve building models. Suggested revision exercise: For a hypothetical client-understanding feature, list possible failure modes and the evidence needed to judge whether its output is useful to desk users.
Risk, compliance, and AI governance
Posting: Initiatives must address controls, documentation, and human oversight before reaching users. Suggested revision exercise: Walk through a hypothetical release decision and distinguish questions for risk, compliance, and human review without assuming a specific RBC approval process.

Skills to revise

Scope an ambiguous desk request
Posting: The manager turns unstructured commercial requests into testable product definitions with success criteria. Suggested practice: Take a vague request to help sales understand clients and write the user problem, boundaries, assumptions, and measurable outcome.
Sequence competing opportunities
Posting: The manager owns roadmap sequencing and documents trade-offs across business and technology requests. Suggested practice: Rank three hypothetical desk requests, then write a short rationale covering value, delivery uncertainty, and what would be deferred.
Write acceptance and evaluation criteria
Posting: The role aligns desk, engineering, data science, and quantitative partners on acceptance criteria and evidence that a capability works for users. Suggested practice: Draft user-facing acceptance criteria and an evaluation plan for one hypothetical pricing-support feature; do not design a model.
Measure adoption and respond to feedback
Posting: The manager tracks adoption and uses post-release feedback about usability, reliability, and practical value. Suggested practice: Define observable adoption measures for a hypothetical desk tool and decide how two contrasting user complaints would affect the next product iteration.
Present a senior-stakeholder recommendation
Posting: Concise communication of options, trade-offs, and recommendations to senior stakeholders is required. Suggested practice: Write a one-page decision brief comparing two scoped AI opportunities, including the recommended sequence and unresolved governance questions.

Your preparation checklist

Prepare an end-to-end product walkthrough
If you have a relevant example, outline the original stakeholder request, discovery, scoped definition, success criteria, delivery, evaluation and adoption. Identify your decisions and actual results; the posting asks for ownership across this full cycle.
Map the desk workflow
Sketch how sales understand clients, how sales and traders set a price, and how price influences a sale. Mark where a desk-facing AI or data product could help and where pricing, trading or risk context matters; treat the sketch as an interview preparation exercise, not RBC’s workflow.
Draft a testable product definition
For one hypothetical desk request, write a one-page brief naming the user, problem, proposed capability, boundaries, success measures and acceptance criteria. Be ready to explain how you would validate the need with desk users and delivery partners.
Practice a roadmap trade-off
Create a short comparison of two hypothetical competing requests, showing sequencing, rationale, dependencies and what you would defer. Rehearse a concise recommendation for senior business and technology stakeholders.
Specify AI evaluation evidence
Prepare an evaluation outline for the hypothetical capability: intended-user tasks, reliability checks, known applied AI or Generative AI limitations, and evidence needed for acceptance. Focus on product decisions and collaboration with engineering, data science and quantitative partners; model building is not part of the advertised role.
Plan controls and post-release learning
List questions you would raise early with risk, compliance and AI governance partners about documentation, controls and human oversight. Add a proposed way to gather desk feedback and measure adoption, usability and reliability after release.
Bring targeted questions for RBC
Ask which Sales & Trading user problems are most pressing, how desk users and delivery partners agree on acceptance criteria, and how the team assesses adoption and governance readiness. These are questions to investigate, not assumptions about RBC’s processes.

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

KEEP EXPLORING

Similar AI jobs

Related skills and specializations in United States of America. Matched to this role, not your profile.

Explore more jobs

Finding similar opportunities…