Associate Product Manager

T-Mobile•Bellevue, WA

8d•Onsite

About The Position

The Associate Product Manager is learning the full craft of product management by doing the work — not just supporting it. Working within a defined feature scope, the APM is accountable for understanding the customer problem behind every feature they own, translating that understanding into clear and executable requirements, and tracking whether the work they shipped moved the metric it was built to move. This is a role for someone who asks "why" before they ask "what." Shipping is not success. Success is when the feature changed customer behavior or moved a business metric, and the APM can show the data to prove it. Day-to-day activities include grounding features in a specific customer problem before writing a single requirement; writing acceptance criteria consumable by engineering; participating in agile ceremonies; building working relationships with PXDI, engineering, and data partners; using AI tools to accelerate documentation and research; and monitoring post-launch metrics to understand whether the feature delivered its intended outcome.

Requirements

Bachelor's Degree plus 2 years of related work experience, or a combination of education and experience deemed equivalent.
Agile Methodology
Backlog Management
Business Acumen
Data Analysis
Market Research
Product Management
Stakeholder Management
Strategic Thinking
Technical Writing
User Experience (UX)

Nice To Haves

0–2 years of relevant Product Management experience in an agile software product development environment.
Customer Engagement
At least 18 years of age
Legally authorized to work in the United States

Responsibilities

Owns a feature end to end — not just the writing of requirements, but whether the feature solved the customer problem it was designed to solve.
Accountable for what ships and what it achieves.
Seeks feedback proactively from the PM, engineering, and design partners and applies it in the next sprint.
Treats every launch as a learning event.
Defines what metric the feature should move before it ships, monitors the data after launch, and brings a point of view on what it shows.
Actively develops product craft and AI fluency, applying AI tools to accelerate documentation, research synthesis, and requirements work.
Participates in PM community of practice.
Maintains a current understanding of the product area, customer experience, and emerging tech trends.
Brings relevant observations to the PM proactively.
Grounds every feature in a specific customer problem before writing a single requirement.
Asks why a problem is worth solving before deciding how to solve it.
Identifies the specific customer behavior the feature is designed to change and articulates why.
Surfaces adjacent customer problems to the PM that aren't yet on the roadmap.
Participates in end-user research and VOC sessions.
Comes with prepared questions and brings structured observations back to the team.
Tests ideas and prototypes with real customers.
Does not consider a feature done until post-launch data confirms the target behavior changed.
Looks for patterns in customer feedback, defect reports, and behavioral data to surface unmet needs and bring them to the PM's attention.
Understands the product strategy and connects their feature work to broader product goals.
Tracks the outcome, not just the delivery.
Measures whether the feature changed customer behavior after launch and connects results back to the product metric it was meant to move.
Participates in development of feature set and positioning strategies.
Contributes a point of view on prioritization based on customer signal and data.
Develops current understanding of the product area and competitive landscape.
Brings relevant market or customer observations to planning conversations.
Builds direct working relationships with PXDI, engineering, and data partners.
Take initiative to align early rather than waiting for the PM to broker the conversation.
Keeps partners informed proactively on feature status, blockers, and outcomes without requiring a formal meeting to do it.
Participates in cross-functional meetings and follows up on action items independently.
Collaborates with stakeholders and execution teams on release planning and feature readiness.
Supports go-to-market with the product knowledge partners need.
Owns communication of their feature to stakeholders: how it performed, what's next, and what the data means.
Specifies which customer behavior should change and how it will be measured.
Communicate clearly in writing and in meetings.
Writes feature documentation, user stories, and status updates that are accurate, concise, and actionable.
After launch, brings the data and a clear read on what it means — not just a summary of what shipped.
Communicates defect status and response to impacted stakeholders until fully resolved.
Reads product analytics independently and knows which metric each feature should move.
Forms a view on the data before presenting it to the team.
Uses AI to accelerate analysis but owns the interpretation and the recommendation.
Monitors post-launch data for features they own.
Identifies gaps between expected and actual performance and brings a hypothesis about why.
Challenges requirements, assumptions, and inherited scope with reasoned pushback.
Brings a well-reasoned alternative when something doesn't make sense.
Escalates the right problems early rather than waiting until they become blockers.
Writes detailed acceptance criteria and feature documentation consumable by engineering.
Uses AI tools to generate first drafts, then critically reviews every output before it moves forward.
Assists with maintaining and grooming the product backlog.
Ensures defects are factored into backlog prioritization based on impact.
Accountable for the quality of what goes downstream — not just whether it was submitted, but whether it was right.
Applies AI tools to core APM work including writing requirements, synthesizing customer research, generating documentation, and analyzing post-launch data — without sacrificing quality or judgment.
Directs AI to generate first drafts, then critically reviews every output before it moves.
Catches gaps that AI misses before they reach engineering.
Stays current on the evolving AI tooling landscape relevant to product work.
Proactively identifies where AI can reduce friction in their workflow.