Growth Product Manager, Insights & Experimentation

Arlo Technologies•Milpitas, CA

1d•$135,000 - $145,000

About The Position

Arlo is seeking a Growth Product Manager, Insights & Experimentation to accelerate understanding of customer behavior, identify growth opportunities, test solutions, and optimize the subscription customer journey. This role is part of the Product-Led Growth team and focuses on increasing learning and execution velocity by identifying where customers realize value, experience friction, and how different segments behave. The position involves owning growth initiatives and experiments end-to-end, analyzing behavior and experiments, identifying follow-on opportunities, and translating customer and product signals into actions that improve activation, trial-to-paid conversion, customer experience, subscriber health, and subscription revenue. This is an ideal role for a data-forward product thinker with a background in growth product management, product analytics, experimentation, lifecycle optimization, or customer insights who wants to operate close to product decisions and turn complex customer behavior into measurable growth opportunities. This is not a traditional feature PM role; the ideal candidate is comfortable working with behavioral data, funnels, cohorts, experiment results, and customer signals to generate hypotheses, product recommendations, experiments, and measurable actions. This is a 1-year fixed-term role.

Requirements

4–6+ years of experience in growth product management, product analytics, experimentation, customer insights, lifecycle optimization, or a related role, ideally within a consumer subscription, SaaS, marketplace, fintech, or connected-device business.
Experience owning or materially driving A/B tests, product experiments, lifecycle tests, or funnel optimization initiatives.
Hands-on experience using product analytics tools such as Amplitude, Mixpanel, Heap, Looker, Tableau, Power BI, or similar platforms.
Strong experience analyzing funnels, cohorts, activation, conversion, feature adoption, retention, churn, customer journeys, or lifecycle behavior.
Experience using behavioral data to investigate ambiguous product or growth problems rather than only reporting predefined metrics.
Ability to translate experiment results, behavioral patterns, and customer signals into product recommendations and follow-on test plans.
Candidates coming from product analytics, experimentation, or insights backgrounds should have demonstrated experience defining hypotheses, influencing product decisions, and driving action through cross-functional teams.
Strong experimentation judgment, including the ability to define hypotheses, success metrics, guardrails, decision criteria, and follow-on tests.
Highly analytical and curious, with the ability to move beyond surface-level reporting and investigate why customer behavior or business performance is changing.
Strong understanding of subscription and growth metrics such as conversion, churn, retention, LTV, ARPU, attach rate, activation, trial-to-paid conversion, feature adoption, and subscriber health.
Ability to segment customer behavior meaningfully and identify differences across lifecycle stages, device types, use cases, product experiences, and customer profiles.
Ability to connect qualitative customer feedback, behavioral data, experiment results, and product experiences to measurable business outcomes.
Strong product judgment and ability to understand customer problems, growth opportunities, tradeoffs, and the “so what” behind the data.
Strong written and verbal communication skills, with the ability to distill complex analysis into clear findings, recommendations, and next actions.
Detail-oriented and process-minded, with strong follow-through on experiment execution, tracking, dashboard monitoring, instrumentation, and action items.
Comfortable operating in a fast-paced environment where systems, data, ownership boundaries, and priorities continue to evolve.
Comfortable working through ambiguity and turning scattered customer and business signals into structured hypotheses and actionable recommendations.
Able to independently own meaningful areas of work while operating within a broader Subscription Growth strategy and collaborating closely with senior product and business leaders.

Nice To Haves

Experience in a device + subscription business or another hybrid hardware/software subscription model.
SQL proficiency or demonstrated ability to self-serve data for analysis.
Experience with Amplitude, LaunchDarkly, Databricks, or similar growth and analytics infrastructure.
Experience working on trial, onboarding, paywalls, checkout, cancellation, win-back, lifecycle, feature activation, or subscription merchandising initiatives.
Experience with app store reviews, in-app surveys, NPS, cancellation surveys, customer feedback intercepts, or Voice-of-Customer programs.
Experience analyzing customer populations where product needs and behaviors differ significantly across use cases, device types, or customer segments.
Background in growth, monetization, subscription lifecycle optimization, customer insights, product-led growth, or experimentation programs.

Responsibilities

Develop a deep understanding of how customers move through the subscription lifecycle, with particular focus on trial activation, value realization, conversion, and early subscriber behavior.
Analyze customer behavior across lifecycle stages, device types, plans, household profiles, feature adoption patterns, and other meaningful cohorts to identify where customers succeed, stall, disengage, or fail to realize subscription value.
Help define and refine meaningful activation and value-realization signals by identifying behaviors that differentiate higher-converting, more engaged, and healthier subscriber cohorts.
Investigate key growth questions around customer needs, conversion gaps, product usage, segmentation, feature adoption, and friction throughout the subscription journey.
Synthesize qualitative signals from app reviews, surveys, cancellation feedback, Care themes, product reviews, and other customer feedback sources, then connect those signals to behavioral data.
Translate customer and behavioral insights into clear product opportunities, hypotheses, segmentation strategies, and experiment recommendations.
Partner with Analytics, Product, Engineering, Marketing, Care, Finance, and other teams to validate findings and identify the highest-value interventions.
Own selected subscription growth experiments end to end across activation, onboarding, conversion, merchandising, paywalls, feature adoption, checkout, cancellation, engagement, and other high-value areas of the subscription journey.
Partner with Product, Engineering, Design, Analytics, Marketing, Care, and Finance to define experiment hypotheses, success metrics, guardrails, launch requirements, and decision criteria.
Go beyond primary metric reporting by analyzing experiment outcomes across key cohorts, lifecycle stages, plans, device types, behavioral segments, and feature adoption patterns.
Determine not only whether an experiment worked, but for whom it worked, why the result may have occurred, what customer behavior changed, and what should happen next.
Translate experiment results into rollout decisions, follow-on tests, iteration opportunities, product recommendations, or roadmap implications.
Own and continuously improve selected growth surfaces or problem areas based on business priorities, such as subscription paywalls, trial experiences, onboarding, feature activation, merchandising, offers, or conversion funnel optimization.
Develop optimization backlogs using funnel data, customer insights, prior experiment learnings, and product opportunities.
Help build a stronger test-and-learn roadmap by connecting experiment results to the next highest-value questions the Growth team should answer.
Maintain experiment tracking, readouts, decision logs, learnings, and follow-up actions.
Analyze subscriber behavior to identify signals that help explain conversion quality, early engagement, cancellation behavior, subscriber health, and downstream retention.
Compare customer cohorts to identify behavioral patterns, product gaps, education opportunities, or lifecycle moments that may influence subscription outcomes.
Help identify leading indicators of healthy subscription behavior and connect those learnings back to upstream activation and conversion opportunities.
Support selected retention, cancellation, save, win-back, or lifecycle experiments where they align with Product-Led Growth priorities.
Partner with Analytics, Billing, Product, Finance, and other teams to connect customer behavior and product interventions to subscriber and revenue outcomes.
Maintain a primary emphasis on understanding and improving the customer journey leading into and through conversion, while using downstream subscriber behavior to improve Growth decision-making.
Support funnel monitoring across trial activation, conversion, feature adoption, checkout, subscriber health, and experiment performance.
Develop recurring diagnostics that surface meaningful movement, anomalies, risks, and opportunities in subscription performance.
Track instrumentation gaps and partner with Product, Engineering, and Analytics to improve event coverage and measurement reliability.
QA analytics events, experiments, and dashboards to ensure Growth initiatives and major product launches can be measured accurately.
Help answer not only what changed, but why it changed and what we should do about it.
Translate behavioral data into concise insights and recommendations for Growth, Product, Engineering, Care, Marketing, Finance, and leadership stakeholders.
Help improve the reliability and accessibility of Growth measurement so teams can make faster and more informed decisions.