Senior Product Designer II, Revenue Experimentation

📍 Job Overview

Job Title: Senior Product Designer II, Revenue Experimentation

Company: Jobgether (Partner Company)

Location: United States

Job Type: Full-time

Category: Product Design / Growth Operations

Date Posted: 2026-09-10

Experience Level: 6+ Years Professional Experience

Remote Status: Fully Remote

🚀 Role Summary

• Design and execute a high volume of experiments focused on subscription growth, pricing, packaging, conversion, retention, and monetization experiences to drive measurable business impact.

• Transform ambiguous ideas and loosely defined briefs into well-scoped, testable hypotheses by leveraging customer insights, data analysis, and user research.

• Act as a strategic and creative partner, leading experiments from conception through to analysis and iteration, influencing the product roadmap.

• Collaborate closely with Product Management, Engineering, Data Science, Analytics, and Content teams throughout the entire experimentation lifecycle.

• Utilize emerging AI tools to accelerate research, ideation, prototyping, production, and experimentation processes, while maintaining accountability for output quality.

📝 Enhancement Note: While the title is "Senior Product Designer II," the responsibilities and focus on "Revenue Experimentation" and "Growth Strategy" indicate a strong overlap with Growth Operations and Product Operations roles. The emphasis on hypothesis generation, data-driven decision-making, and cross-functional collaboration with data science and analytics are key indicators of an operations-centric design function.

📈 Primary Responsibilities

• Design and execute a high volume of A/B or multivariate experiments across critical revenue-generating journeys, including trial conversion, trial-to-paid conversion, pricing strategies, SKU optimization, churn prevention, and win-back initiatives.

• Proactively generate new experiment concepts and hypotheses based on market trends, customer insights, analytics data, and strategic business objectives, moving beyond predefined briefs.

• Lead the end-to-end experimentation process for owned initiatives, from hypothesis formulation and user flow design to prototype development, usability testing, and final implementation.

• Partner with Data Science and Analytics teams to define key success metrics, establish rigorous experiment methodologies, interpret complex outcomes, and translate findings into actionable follow-up hypotheses and product recommendations.

• Engage Engineering teams early in the ideation and design phases to identify technical constraints, explore innovative solutions, and ensure feasibility for rapid deployment and iteration.

• Present experiment concepts, design rationale, progress updates, learnings, and measurable outcomes clearly and concisely to cross-functional stakeholders and leadership.

• Maintain meticulously organized and traceable design work, including clean and well-structured Figma files, documented hypotheses, decision rationale, and a comprehensive experiment history for future reference and knowledge sharing.

• Leverage AI tools as a creative and productivity partner for research synthesis, ideation brainstorming, prompt engineering, rapid prototyping, and experiment analysis, continuously evaluating and adopting new AI capabilities to enhance team velocity and quality.

📝 Enhancement Note: The responsibilities emphasize a high degree of autonomy and proactivity in generating experimental ideas, which is characteristic of senior roles in growth-focused operations. The requirement to translate "ambiguous ideas into clear hypotheses" and "actionable follow-up hypotheses" highlights a strategic operations mindset.

🎓 Skills & Qualifications

Education:

• Bachelor's degree in Design, Human-Computer Interaction, Psychology, Computer Science, or a related field, or equivalent practical experience. Experience:

• A minimum of 6 years of professional product design experience, with a significant portion dedicated to growth, experimentation, subscription models, or monetization strategies.

• Proven track record of successfully shipping and iterating on A/B or multivariate experiments within a fast-paced, high-velocity product development environment.

• Demonstrated ability to navigate ambiguity, transforming incomplete briefs into well-defined, testable problems and robust hypotheses through effective use of research and data analytics. Required Skills:

• Advanced proficiency in Figma for wireframing, prototyping, and high-fidelity design, with strong visual design skills and hands-on animation capabilities.

• Expertise in hypothesis generation and experimental design, with a deep understanding of A/B testing, multivariate testing, and statistical significance.

• Ability to translate complex business goals and customer needs into clear, actionable experiment designs and user flows.

• Strong analytical skills to interpret experiment results, identify key drivers of success or failure, and formulate data-driven recommendations for future iterations.

• Excellent organizational habits and meticulous attention to detail, ensuring clean design files, clear documentation, and traceable decision-making processes.

• Proactive, ideas-driven mindset with the confidence to generate and champion new experiment concepts independently.

• Strong strategic and collaborative skills, capable of advocating for design solutions while constructively challenging ideas from Product, Engineering, and other partners.

• Comfort and proficiency in using AI tools for research, analysis, ideation, prototyping, and production, with a commitment to continuous learning and adoption of new AI capabilities.

• Superior communication skills, enabling clear articulation of design rationale, experiment methodology, findings, and strategic recommendations to diverse audiences, including technical and non-technical stakeholders. Preferred Skills:

• Familiarity with experimentation and analytics platforms such as Statsig, Optimizely, Amplitude, Mixpanel, or similar tools.

• Experience with user research methodologies, including qualitative interviews, surveys, and usability testing, to inform hypothesis generation and design validation.

• Understanding of subscription business models, SaaS monetization strategies, and key metrics related to customer acquisition, retention, and lifetime value.

• Experience in an AI-native organization, contributing to the development of AI-assisted design and experimentation practices.

📝 Enhancement Note: The requirement for "6+ years of professional product design experience, ideally including growth, experimentation, subscription, or monetization-focused work" strongly aligns with senior-level operations roles. The portfolio requirement specifically asks for demonstration of "experimentation expertise, including problem framing, hypotheses, iteration, design decisions, and measurable business or customer impact," which is a core component of operations assessment.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• A dedicated section showcasing your expertise in designing and executing revenue-focused experiments.

• Clear documentation of at least 3-5 distinct experiment case studies, illustrating your end-to-end process.

• For each case study, detail the initial problem or ambiguous brief, the formulated hypothesis, the design solutions developed, the experiment methodology employed (e.g., A/B test, multivariate), and the measurable business or customer impact achieved.

• Demonstrate your ability to translate data insights and customer research into testable hypotheses and design iterations.

• Showcase examples of how you collaborated with Product, Engineering, and Data Science/Analytics teams throughout the experimentation lifecycle. Process Documentation:

• Provide evidence of your process for transforming ambiguous inputs into structured, testable hypotheses and well-scoped experiment designs.

• Illustrate your approach to user research and data analysis for hypothesis validation and iteration planning.

• Showcase your workflow for collaborating with engineering and product teams to ensure successful experiment implementation and launch.

• Include examples of how you have documented experiment results, learnings, and recommended next steps to inform product strategy and future experimentation.

• Demonstrate your proficiency in using AI tools to streamline and enhance your design and experimentation processes.

📝 Enhancement Note: The emphasis on "experimentation expertise, including problem framing, hypotheses, iteration, design decisions, and measurable business or customer impact" is a direct call for a portfolio that reflects operational rigor and data-driven outcomes, beyond just aesthetic design. This aligns perfectly with the requirements for operations professionals.

💵 Compensation & Benefits

Salary Range:

• Estimated Range: $152,000 - $223,000 CAD annually.

• Note: The provided salary range is in Canadian Dollars (CAD) as stated in the job description. However, the role specifies "based in United States" and "Remote-first work environment within US." It's crucial to clarify if the compensation is adjusted for US cost of living and paid in USD, or if it's a Canadian salary for a US-based remote role. For US operations roles at this experience level, a typical range might be $160,000 - $240,000 USD annually, depending on the specific location within the US and the company's compensation philosophy.

Benefits:

• Equity: Stock options or other equity grants as part of the overall compensation package.

• Health & Wellness:

  • Supplemental medical and dental coverage options.
  • Mental Wellness Program and Employee Assistance Program (EAP) for comprehensive support.

• Retirement Savings: RRSP with DPSP (Deferred Profit Sharing Plan) plan.

• Work-Life Balance:

  • Paid parental leave.
  • Flexible paid time off (PTO).
  • Company-wide holidays.
  • Dedicated summer and winter shutdown periods.

• Professional Development: Learning and development programs to support ongoing professional growth and skill enhancement.

• Remote Work Support: Equipment, tools, and reimbursement support for an effective and productive remote work environment.

• Exclusive Perks: Free premium membership and connected-device benefits.

• AI-Native Environment: Opportunity to work with and develop modern AI-assisted design and experimentation practices.

Working Hours:

• Standard full-time hours are assumed to be approximately 40 hours per week, with flexibility often inherent in remote, results-oriented roles. The emphasis on "fast-paced and highly collaborative" suggests a dynamic work environment where focus and efficiency are key.

📝 Enhancement Note: The salary is listed in CAD, but the role is in the US. This discrepancy requires clarification. Based on US market rates for a Senior Product Designer II with 6+ years of experience in a high-demand area like revenue experimentation, the compensation would typically be benchmarked against US market data. For example, in a major tech hub, a Senior Product Designer can earn anywhere from $150,000 to $250,000+ USD annually, with equity. The provided CAD range translates to approximately $111,000 - $163,000 USD, which may be on the lower end for a US-based senior role, especially considering the specialized nature of revenue experimentation.

🎯 Team & Company Context

🏢 Company Culture

Industry: Technology / Software (AI-Native Organization)

Company Size: The description implies a growing, established tech company with a global reach ("subscription growth at global scale," "products used by millions of people worldwide"), likely fitting within the mid-to-large enterprise category if considering its partner. Jobgether itself functions as a platform, so its own size might differ.

Founded: The founding date of the partner company is not specified, but the context suggests an established entity with a focus on modern technology and AI.

Team Structure:

• Product Design Team: A remote-first product design team with a focus on growth, experimentation, and subscription monetization. The "II" in the title suggests a mid-to-senior level within the design hierarchy.

• Cross-Functional Collaboration: Operates within a highly collaborative environment, working daily with Product Managers, Engineers, Data Scientists, Analysts, and Content teams. This structure is typical of agile product development organizations focused on data-driven iteration.

• Reporting Structure: Likely reports into a Head of Design or Director of Product Design, with a dotted line to Product Management and Growth leads for specific experiment initiatives.

Methodology:

• Data-Driven Experimentation: Core methodology revolves around designing, executing, and analyzing high-volume experiments (A/B, multivariate) to inform product decisions and drive growth.

• Hypothesis-Led Development: A strong emphasis on transforming ambiguous ideas into clear, testable hypotheses before investing in design and engineering.

• Agile & Iterative: The fast-paced, iterative nature of experimentation requires agile workflows, rapid prototyping, and continuous learning.

• AI-Augmented Processes: Integration of AI tools across the design and experimentation lifecycle for efficiency and enhanced capabilities.

Company Website: https://jobgether.com/ (Jobgether platform)

Partner Company Website: Not specified, but likely a SaaS or subscription-based technology company.

📝 Enhancement Note: The "AI-native working environment" and explicit mention of using "emerging AI tools" suggest a forward-thinking culture that embraces technological advancement, which is a significant differentiator. The focus on "revenue experimentation" and "subscription growth" indicates the company is highly focused on optimizing its core business drivers.

📈 Career & Growth Analysis

Operations Career Level: This role is positioned as a Senior Product Designer II, indicating a significant level of expertise and autonomy. It sits at the intersection of design craft and strategic operations, focusing on driving measurable business outcomes through experimentation. It's a role that requires not just design execution but also strategic thinking, hypothesis generation, and data interpretation.

Reporting Structure: The designer will report to a design leadership role (e.g., Head of Product Design) but will work very closely with Product Managers, Data Scientists, and Engineers on specific experiment pods or initiatives. This matrixed reporting structure is common in growth-focused teams.

Operations Impact: The primary impact of this role is on revenue generation and growth metrics. By designing and executing experiments across pricing, conversion, retention, and monetization, the designer directly influences key business KPIs such as conversion rates, average revenue per user (ARPU), customer lifetime value (CLTV), and churn reduction. The ability to identify and validate impactful growth levers makes this a high-impact position.

Growth Opportunities:

• Specialization in Growth & Experimentation: Deepen expertise in growth strategies, experimentation methodologies, and data analysis within a high-volume environment.

• AI-Assisted Design Leadership: Become a leader in leveraging AI tools for design and experimentation, potentially developing best practices for the team and organization.

• Cross-Functional Influence: Develop strong strategic partnerships with Product, Data Science, and Engineering, gaining broader business acumen and influencing product roadmaps.

• Leadership Potential: Progress to a Lead Product Designer role or transition into a Product Operations or Growth Operations management track, leveraging a deep understanding of experimentation and user behavior.

• Industry Recognition: Contribute to a company operating at global scale, gaining experience with complex challenges and potentially building a portfolio that is highly attractive in the market.

📝 Enhancement Note: The role is not a traditional "operations" title, but the responsibilities and required skills—particularly around hypothesis testing, data interpretation, and driving measurable business impact through experimentation—are core to a sophisticated Revenue Operations or Growth Operations function. The growth opportunities reflect a path towards more strategic operational leadership.

🌐 Work Environment

Office Type: Remote-first work environment. This means the company is structured to support remote employees as their primary mode of work, rather than treating remote as an exception.

Office Location(s): Primarily within the United States, offering flexibility for candidates across different US time zones. While remote, there might be an expectation for occasional in-person collaboration or team gatherings, though this is not explicitly stated.

Workspace Context:

• Collaborative Digital Space: Expect a highly digital and collaborative environment, relying heavily on tools like Figma, Slack, video conferencing, and project management software to maintain seamless communication and teamwork.

• Technology Stack: Access to a robust technology stack including advanced design tools (Figma), experimentation platforms (Statsig, Optimizely), analytics tools (Amplitude), and AI-powered productivity tools.

• Team Interaction: Regular interaction with a diverse, global team, fostering an inclusive culture that values different perspectives and direct communication. Opportunities to engage with peers for feedback, knowledge sharing, and collaborative problem-solving.

Work Schedule: While a standard 40-hour work week is implied, the remote-first, fast-paced nature of experimentation often allows for flexibility in scheduling to accommodate deep work sessions, cross-time zone collaboration, and personal needs, provided that deadlines and team availability are met. The focus is on delivering results and driving impact.

📝 Enhancement Note: The "remote-first" approach for a US-based team suggests a mature remote work infrastructure and culture, which is highly appealing for operations professionals who often value autonomy and flexibility.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening (Jobgether AI): Your application will be processed by Jobgether's AI system for initial objective matching against core requirements.

• Hiring Company Review: Shortlisted candidates are shared directly with the partner company's internal hiring team.

• Design Challenge/Portfolio Review: Expect a rigorous review of your portfolio, specifically focusing on your experimentation case studies. This will likely involve presenting your process, design decisions, hypotheses, and measurable outcomes. A design exercise or a more in-depth case study presentation may be required.

• Cross-Functional Interviews: Interviews with Product Managers, Engineers, Data Scientists, and potentially other designers to assess collaboration skills, strategic thinking, and understanding of the experimentation lifecycle.

• Leadership Interview: A final interview with design or product leadership to assess cultural fit, strategic alignment, and overall senior-level capabilities.

Portfolio Review Tips:

• Highlight Experimentation: Dedicate a significant portion of your portfolio to showcasing your experience with A/B testing, hypothesis generation, and data-driven iteration.

• Quantify Impact: For each experiment case study, clearly articulate the problem, your solution, the methodology, and most importantly, the measurable business or customer impact (e.g., % increase in conversion, $ saved, % reduction in churn). Use specific numbers and metrics.

• Showcase the Process: Detail your thought process—how you framed problems, generated hypotheses, conducted research, iterated on designs, and collaborated with teams. Don't just show final polished screens.

• Demonstrate Ambiguity Navigation: Include examples where you took loosely defined problems or ambiguous briefs and systematically turned them into actionable experiments.

• AI Tool Integration: If applicable, highlight how you've used AI tools to enhance your workflow, research, or ideation process.

• Conciseness and Clarity: Ensure your portfolio is well-organized, easy to navigate, and clearly communicates your value proposition.

Challenge Preparation:

• Understand the Business: Research Jobgether and the likely industry of their partner company. Understand common subscription growth challenges, monetization strategies, and experimentation best practices in that space.

• Hypothesis Formulation: Practice generating hypotheses for common growth scenarios (e.g., improving trial sign-ups, increasing upgrade rates).

• Metrics Definition: Be prepared to discuss key metrics for revenue experimentation and how you would measure success.

• AI Prompting: Familiarize yourself with effective prompting strategies for design and research tasks using AI tools.

• Communication: Practice articulating complex ideas, design decisions, and experiment results clearly and concisely.

📝 Enhancement Note: The emphasis on a "portfolio demonstrating experimentation expertise, including problem framing, hypotheses, iteration, design decisions, and measurable business or customer impact" is a critical requirement that operations candidates should highlight. This is distinct from a portfolio focused solely on UI/UX aesthetics.

🛠 Tools & Technology Stack

Primary Tools:

• Figma: Advanced proficiency is a core requirement for wireframing, prototyping, and high-fidelity design.

• AI Tools: Explicitly mentioned as a key component of the workflow. This could include generative AI for ideation (e.g., Midjourney, DALL-E), text generation for copy/hypotheses (e.g., ChatGPT, Claude), AI-assisted research tools, and potentially AI-powered prototyping tools.

• Prototyping Tools: While Figma is primary, other prototyping tools might be used or beneficial.

Analytics & Reporting:

• Experimentation Platforms: Familiarity with platforms like Statsig, Optimizely, VWO, Adobe Target is highly advantageous.

• Product Analytics Tools: Experience with Amplitude, Mixpanel, or similar tools for understanding user behavior, tracking experiment results, and deriving insights.

• Data Visualization Tools: Potential use of tools like Tableau, Looker, or Power BI for analyzing and presenting experiment data, though this might be primarily handled by Data Science/Analytics teams.

CRM & Automation:

• CRM: While not explicitly mentioned, familiarity with CRM systems (e.g., Salesforce, HubSpot) can be beneficial for understanding customer data and journey context, especially for subscription models.

• Automation Tools: The role's focus on efficiency and AI suggests an appreciation for automation, though specific tools beyond AI are not listed.

📝 Enhancement Note: The explicit mention of "Statsig, Optimizely, Amplitude, or similar tools" provides concrete examples of the experimentation and analytics stack. The emphasis on "AI tools" signifies a modern, tech-forward environment where proficiency with AI assistants is becoming a standard operational skill.

👥 Team Culture & Values

Operations Values:

• Customer-Centered Decision-Making: All design and experimentation efforts are driven by a desire to improve the customer experience and deliver value.

• Data-Driven Approach: Decisions are rigorously informed by data, analytics, and the outcomes of well-designed experiments.

• Efficiency and Velocity: A commitment to rapid iteration, high-volume experimentation, and leveraging technology (including AI) to accelerate processes.

• Meaningful Impact: Focus on driving tangible business results and contributing to the company's growth objectives.

• Direct and Respectful Communication: An open and honest communication style is encouraged, fostering a collaborative environment where feedback is shared constructively.

Collaboration Style:

• Cross-Functional Integration: Seamless collaboration across design, product, engineering, data science, and analytics is fundamental. This involves proactive communication, shared ownership of experiment outcomes, and a unified approach to problem-solving.

• Experimentation Culture: A culture that embraces testing, learning, and iterating. Failure is viewed as a learning opportunity, encouraging bold experimentation.

• Knowledge Sharing: Active sharing of AI workflows, prompting strategies, experiment learnings, and design best practices among team members to foster collective growth and efficiency.

📝 Enhancement Note: The values emphasize a blend of user-centricity, data-informed strategy, and operational efficiency, which are hallmarks of successful revenue and growth operations teams. The "AI-native working environment" suggests a culture that is forward-looking and embraces technological innovation.

⚡ Challenges & Growth Opportunities

Challenges:

• High Volume of Experiments: Managing and executing a high volume of experiments simultaneously requires exceptional organizational skills, prioritization, and efficient workflows.

• Navigating Ambiguity: Consistently translating vague ideas or briefs into clear, testable hypotheses in a fast-paced environment.

• Balancing Design Craft and Speed: Maintaining high standards of design quality and user experience while operating at a rapid pace demanded by experimentation.

• Interpreting Complex Data: Effectively interpreting experiment outcomes, especially those with nuanced results or unexpected findings, and translating them into actionable insights.

• AI Integration Curve: Continuously learning and adapting to new AI tools and workflows, integrating them effectively without compromising quality or accountability.

Learning & Development Opportunities:

• Specialized Growth Expertise: Deepen knowledge in growth hacking, conversion rate optimization (CRO), subscription models, and monetization strategies.

• Advanced Experimentation Techniques: Gain mastery in complex experimentation methodologies and statistical analysis.

• AI in Design & Experimentation: Become a pioneer in applying AI to product design and experimentation, potentially leading AI adoption initiatives.

• Cross-Functional Acumen: Develop a comprehensive understanding of product development, data science, and business strategy through close collaboration.

• Leadership Development: Opportunities to mentor junior designers, lead experiment initiatives, and potentially move into design leadership or operations management roles.

📝 Enhancement Note: The challenges highlight the operational demands of the role, requiring sophisticated project management and analytical skills beyond traditional design. The growth opportunities point towards specialization in high-value areas like AI and advanced experimentation.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you turned an ambiguous business problem into a well-defined, testable hypothesis. What was the outcome?"

• "Walk us through an experiment you designed that significantly impacted a key revenue metric. What was your process, and how did you measure success?"

• "How do you balance the need for rapid experimentation with maintaining high standards of design quality and user experience?"

• "How have you leveraged AI tools in your design or experimentation process? Provide a specific example of how it improved efficiency or outcomes."

• "Describe your approach to collaborating with Product Managers, Engineers, and Data Scientists on a complex experiment. What challenges did you face, and how did you overcome them?" Company & Culture Questions:

• "What excites you about working in a remote-first, AI-native environment focused on revenue experimentation?"

• "How do you stay current with the latest trends in product design, experimentation, and AI?"

• "Describe your ideal team collaboration style. How do you ensure effective communication and alignment across different functions?"

• "How do you approach receiving and giving feedback on design and experiment proposals?" Portfolio Presentation Strategy:

• Structure: Organize your presentation around 2-3 key experiment case studies. For each, clearly outline: Problem -> Hypothesis -> Design Solution(s) -> Methodology -> Results -> Learnings/Next Steps.

• Quantify Impact: Emphasize the measurable business impact (e.g., conversion lift, revenue increase) with specific data points.

• Show Your Thinking: Explain why you made certain design decisions and how you arrived at your hypotheses. Discuss trade-offs and alternative approaches considered.

• Highlight Collaboration: Briefly mention how you partnered with other teams (PM, Eng, Data) at different stages of the experiment.

• AI Integration: If applicable, weave in how AI tools supported your process (e.g., faster ideation, content generation, analysis support).

• Conciseness: Be mindful of time. Focus on the most impactful aspects of your work.

📝 Enhancement Note: The interview preparation focuses on demonstrating operational capabilities: problem-solving, data-driven decision-making, cross-functional collaboration, and the ability to quantify impact—all critical for operations roles.

📌 Application Steps

To apply for this operations-aligned design position:

• Submit your application through the provided Jobgether link.

• Tailor your Resume: Highlight your experience in growth, experimentation, A/B testing, and driving measurable business impact. Use keywords from the job description.

• Curate Your Portfolio: Ensure your portfolio prominently features 2-3 detailed case studies of revenue experiments you've designed and executed. Quantify the results and clearly articulate your process from hypothesis to impact.

• Prepare Your Presentation: Practice presenting your portfolio case studies, focusing on clear articulation of your strategy, execution, and the quantifiable business outcomes achieved. Be ready to discuss your collaboration process with cross-functional teams.

• Research the Partner Company: If possible, research the industry and common challenges faced by the partner company to better tailor your responses and demonstrate strategic understanding.

• Understand AI Integration: Be prepared to discuss your experience and perspective on using AI tools in design and experimentation workflows.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.