Senior Product Designer II, Revenue Experimentation

📍 Job Overview

Job Title: Senior Product Designer II, Revenue Experimentation

Company: Jobgether (Partner Company)

Location: Canada

Job Type: Full-time

Category: Product Design / Revenue Experimentation

Date Posted: 2026-09-10

Experience Level: 6+ Years (Mid-Senior to Senior)

Remote Status: Remote Solely (Within Canada)

🚀 Role Summary

• Design and execute a high volume of experiments focused on subscription growth, conversion, pricing, and monetization across a global user base.

• Transform ambiguous business needs and customer insights into clear, testable hypotheses and thoughtful user experiences that drive measurable impact.

• Act as a strategic partner, collaborating closely with Product Management, Engineering, Data Science, Analytics, and Content teams throughout the entire experiment lifecycle.

• Leverage AI-native tools to accelerate research, ideation, prototyping, production, and experimentation, while maintaining high standards of design craft and quality.

• Contribute to the product roadmap by generating new experiment concepts and informing data-driven decision-making.

📝 Enhancement Note: This role is highly specialized within product design, focusing specifically on revenue experimentation. The emphasis on "high-volume experiments" and "measurable impact" indicates a strong need for data-driven design thinking and a comfort with iterative testing cycles. The mention of an "AI-native organization" suggests a forward-thinking environment where leveraging AI for design and productivity is not just encouraged but expected. The target audience for this role would be experienced product designers with a proven track record in growth, experimentation, and a deep understanding of subscription models and monetization strategies.

📈 Primary Responsibilities

• Lead the design and execution of a high volume of experiments across key revenue drivers, including trial conversion, trial-to-paid conversion, pricing strategies, SKU optimization, churn prevention, and win-back initiatives.

• Proactively translate loosely defined briefs into well-scoped, testable hypotheses by leveraging user research, analytics, and customer insights to validate or challenge assumptions.

• Champion new experiment concepts originating from personal ideation, while also serving as a strategic and creative partner on ideas proposed by Product Managers and other stakeholders.

• Collaborate with Data Science and Analytics teams to define success metrics, rigorously interpret experiment outcomes, and translate findings into actionable follow-up hypotheses and product iterations.

• Partner closely with Product Managers, Engineers, Content Strategists, and fellow designers from concept ideation and prototyping through implementation, bug bashes, launch, and comprehensive results analysis.

• Clearly present experiment concepts, progress, key learnings, and outcomes to cross-functional teams and leadership, ensuring alignment and informed decision-making.

• Maintain meticulously organized and traceable design work, including clean and structured Figma files, documented hypotheses, decision rationale, and a clear history of experiment iterations.

• Engage proactively with engineering teams early in the design process to identify potential technical constraints and opportunities, thereby preventing future delivery blockers.

• Utilize emerging AI tools as a creative and productivity partner for research, analysis, drafting content, prototyping, experimentation, and production work, while retaining full accountability for the quality and impact of all outputs.

• Actively share effective AI workflows, automation techniques, and prompting strategies with teammates, and continuously evaluate new tools to enhance team velocity and output quality.

📝 Enhancement Note: The responsibilities highlight a blend of strategic thinking, hands-on design execution, and strong collaboration. The emphasis on "proactively translate loosely defined briefs" and "champion new experiment concepts" suggests a need for initiative and a willingness to drive projects from inception. The requirement to "maintain meticulously organized and traceable design work" points to the importance of documentation and design process rigor, crucial for experimentation.

🎓 Skills & Qualifications

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

Experience: 6+ years of professional product design experience, with a significant portion ideally focused on growth, experimentation, subscription services, or monetization strategies.

Required Skills:

• Demonstrated experience in shipping and iterating on A/B or multivariate experiments within a fast-moving, high-velocity product environment.

• Strong ability to navigate and thrive in ambiguity, transforming incomplete briefs into well-defined, testable problems and hypotheses using research and data.

• Proactive, ideas-driven approach with the confidence to generate novel experiment concepts, rather than solely relying on predefined briefs.

• Excellent strategic and collaborative skills, capable of championing personal ideas and constructively challenging those of Product and other partners.

• Proven experience working closely with Engineering, Product, Content, Data Science, and Analytics teams throughout the complete experimentation lifecycle.

• Exceptional organizational habits and meticulous attention to detail, demonstrated through well-structured Figma files and clear documentation of design decisions and rationale.

• Advanced proficiency in Figma for all design and prototyping activities.

• Strong visual design skills complemented by hands-on animation capabilities for creating engaging user experiences.

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

• Ability to effectively manage and balance multiple concurrent experiments while upholding high standards of design quality and execution.

• Strong communication skills, with the ability to articulate design rationale, experiment methodology, findings, and recommendations clearly to diverse audiences. Preferred Skills:

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

• Experience with user research methodologies and synthesis.

• Understanding of subscription models, SaaS economics, and conversion funnels.

• Experience in an AI-native or AI-forward organization.

📝 Enhancement Note: The required skills emphasize a blend of technical design proficiency (Figma, animation), strategic thinking (hypothesis generation, ambiguity navigation), and collaborative execution. The preference for specific experimentation platforms indicates a desire for candidates who can hit the ground running with minimal ramp-up time. The explicit mention of AI tool proficiency is a key differentiator.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Showcase case studies that clearly demonstrate expertise in experimentation, not just polished final designs.

• For each case study, detail the problem framing, the hypotheses generated, the iterative design process, key design decisions made, and crucially, the measurable business or customer impact achieved.

• Provide evidence of your ability to work with ambiguity and transform loosely defined problems into structured, testable experiments.

• Include examples of how you've used data and customer insights to validate or challenge assumptions and inform design iterations.

• Demonstrate your process for collaborating with Product, Engineering, Data Science, and Analytics teams throughout the experimentation lifecycle. Process Documentation:

• Examples of well-structured Figma files that are organized, traceable, and clearly document design rationale.

• Documentation of experiment hypotheses, including the rationale behind them and the anticipated outcomes.

• Evidence of your process for translating experiment findings into actionable follow-up hypotheses and product iterations.

• Showcase how you engage engineers early to identify technical constraints and opportunities.

• If applicable, include examples of AI workflows or prompting strategies used to enhance design productivity and quality.

📝 Enhancement Note: The portfolio requirements are heavily geared towards demonstrating a structured, data-informed approach to experimentation. Candidates must go beyond showcasing aesthetics and instead focus on the strategic thinking, execution, and measurable results of their work in an experimental context. The emphasis on collaboration and documentation is also critical.

💵 Compensation & Benefits

Salary Range: CAD $176,000 – $207,000 annually. This range is competitive for a Senior Product Designer II role in Canada, reflecting the specialized nature of revenue experimentation and the advanced experience required. Final compensation will be determined based on factors including candidate experience, skills, knowledge, and specific location within Canada.

Benefits:

• Comprehensive Health Coverage: Medical, dental, vision, life, and disability insurance.

• Retirement Savings: RRSP with DPSP plan.

• Family Support: Paid parental leave.

• Wellness & Support: Mental Wellness Program and Employee Assistance Program (EAP).

• Time Off: Flexible paid time off, company-wide holidays, and dedicated summer and winter shutdown periods.

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

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

• Equity: Stock options as part of the overall compensation package.

• Premium Membership: Free premium membership and connected-device benefits.

Working Hours: Standard full-time hours (approximately 40 hours per week), with flexibility expected to manage a high volume of experiments and cross-functional collaboration.

📝 Enhancement Note: The salary range provided is specific to Canada and aligns with senior-level design roles in tech. The benefits package is comprehensive, offering strong support for health, retirement, and work-life balance, which are key attractors for experienced professionals. The inclusion of equity and a focus on remote work support are also significant.

🎯 Team & Company Context

🏢 Company Culture

Industry: Technology / AI-Native Organization. The company operates within the tech sector, with a strong emphasis on leveraging Artificial Intelligence across its product development and operational processes. This AI-native approach influences workflows, tools, and the overall pace of innovation.

Company Size: Not explicitly stated, but the role is within a "remote-first product design team" and the compensation/benefits suggest a well-established, likely mid-to-large size company.

Founded: Not explicitly stated, but the "AI-native" and "global scale" descriptors suggest a company that is likely established and has achieved significant growth.

Team Structure:

• The role is part of a "remote-first product design team" with a focus on "shaping subscription growth." This implies a specialized design unit.

• Collaboration is expected across Product, Engineering, Data Science, Analytics, and Content teams, indicating a highly cross-functional and integrated approach to product development and experimentation.

• The reporting structure is not detailed, but the "Senior Product Designer II" title suggests a mid-to-senior level individual contributor role, likely reporting to a Design Lead or Manager. Methodology:

• Data-Driven Experimentation: Core to the team's approach is the design and execution of high-volume experiments (A/B, multivariate) to inform product strategy and drive measurable impact.

• Hypothesis-Led Design: Ambiguous ideas are transformed into clear, testable hypotheses, with data and research serving as the foundation for decision-making.

• AI-Assisted Workflows: The team actively uses AI tools to accelerate various stages of the design and experimentation process, from research and ideation to prototyping and analysis.

• Agile & Iterative: The fast-paced, iterative nature of experimentation requires agility and a continuous improvement mindset.

Company Website: [Jobgether Website - though the role is for a partner company]

📝 Enhancement Note: The company culture is characterized by its AI-native approach, remote-first setup, and a strong emphasis on data-driven experimentation for revenue growth. This suggests an environment that values innovation, efficiency, and measurable outcomes. Collaboration is key, with designers working closely with technical and analytical teams.

📈 Career & Growth Analysis

Operations Career Level: Senior Product Designer II. This level signifies a senior individual contributor role with significant autonomy and responsibility for driving complex projects and influencing product strategy. It involves not only executing design but also proactively identifying opportunities and mentoring others.

Reporting Structure: While not explicitly defined, this role likely reports to a Design Manager or Lead within the Product Design team. The emphasis on collaboration means significant interaction with Product Managers, Engineering Leads, and Data Science/Analytics Leads.

Operations Impact: The role has a direct and significant impact on revenue growth through the design and execution of experiments focused on conversion, pricing, retention, and monetization. Success in this role directly contributes to key business metrics and informs the strategic direction of product development in revenue-generating areas.

Growth Opportunities:

• Specialization Advancement: Deepen expertise in revenue experimentation, A/B testing methodologies, and subscription growth strategies, potentially becoming a subject matter expert.

• Leadership Development: Opportunity to lead larger, more complex experimentation initiatives, mentor junior designers, and influence design processes and best practices within the team.

• AI Skill Enhancement: Continuous learning and application of cutting-edge AI tools in design and experimentation, positioning the candidate at the forefront of emerging design practices.

• Cross-Functional Influence: Grow influence across product, engineering, and data science by consistently delivering impactful insights and driving strategic decisions through well-executed experiments.

📝 Enhancement Note: The "Senior Product Designer II" title suggests a role with substantial impact and growth potential. The focus on revenue experimentation and AI integration offers opportunities for specialized skill development and leadership within a forward-thinking tech environment.

🌐 Work Environment

Office Type: Remote Solely. The role is fully remote, requiring candidates to be based within Canada. This offers significant flexibility but also necessitates strong self-discipline and proactive communication.

Office Location(s): Canada. The role is open to candidates located anywhere within Canada, leveraging a distributed team model.

Workspace Context:

• Collaborative Digital Space: The remote-first environment relies heavily on digital collaboration tools (e.g., Slack, Figma, video conferencing) for communication, design reviews, and team syncs.

• AI-Integrated Tools: Access to and utilization of AI tools will be a daily part of the workflow, enhancing productivity and creative output.

• Cross-Functional Interaction: Regular opportunities to connect and collaborate with colleagues across different departments (Product, Engineering, Data Science, Analytics, Content) to drive experimentation initiatives.

Work Schedule: While specific working hours are not detailed beyond the standard 40-hour week, the nature of experimentation and global collaboration may require some flexibility to accommodate different time zones and urgent testing needs.

📝 Enhancement Note: The remote-first nature of this role in Canada emphasizes the need for strong asynchronous communication skills and self-management. The workspace is digitally driven, with AI tools playing a significant role in daily tasks.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening (Jobgether AI): An AI-powered matching process will quickly assess applications against core requirements. Top candidates are then shortlisted and shared with the hiring company.

• Hiring Company Review: The partner company's internal team will review shortlisted candidates.

• Design Challenge/Portfolio Review: Expect a deep dive into your portfolio, focusing on experimentation case studies. This may involve a presentation where you explain your process, hypotheses, design decisions, and measurable impact. A practical design challenge related to experimentation might also be assigned.

• Cross-Functional Interviews: Interviews with Product Managers, Engineers, Data Scientists, and potentially other designers to assess collaboration style, strategic thinking, and technical acumen.

• Leadership Interview: A final interview with a design leader or senior stakeholder to evaluate cultural fit, strategic vision, and overall suitability for the Senior Product Designer II role.

Portfolio Review Tips:

• Focus on Experimentation: Your portfolio must prominently feature case studies detailing your experience with A/B testing, hypothesis generation, and iterative design based on experimental results.

• Quantify Impact: Clearly articulate the measurable business or customer outcomes achieved through your experimental designs. Use metrics and data to support your claims.

• Showcase Process: Detail your thought process: how you framed problems, developed hypotheses, iterated on designs, collaborated with teams, and interpreted results.

• Document Rationale: Ensure your design decisions are well-explained, especially how they tie back to hypotheses and data insights.

• Figma & AI Examples: Be prepared to walk through your Figma files and discuss how you've leveraged AI tools in your workflow.

Challenge Preparation:

• Hypothesis Generation: Practice formulating clear, testable hypotheses for common growth and monetization scenarios.

• Data Interpretation: Be ready to discuss how you would interpret results from hypothetical experiments and what follow-up actions you might recommend.

• AI Application: Consider how you would use AI for research, ideation, or prototyping in a given scenario.

• Communication: Practice explaining complex experimental concepts and findings concisely to both technical and non-technical audiences.

📝 Enhancement Note: The application process leverages AI for initial screening, with the core evaluation handled by the partner company. The portfolio review is critical and must directly address the requirements of revenue experimentation. Candidates should prepare to demonstrate their strategic thinking and measurable impact through concrete examples.

🛠 Tools & Technology Stack

Primary Tools:

• Figma: Advanced proficiency is a mandatory requirement for all design and prototyping work.

• AI Tools: Significant use of emerging AI tools for research, analysis, drafting, prototyping, and production is expected. Candidates should be comfortable and proactive in leveraging these.

Analytics & Reporting:

• Experimentation Platforms: Familiarity with platforms like Statsig, Optimizely, or similar is advantageous.

• Analytics Platforms: Experience with tools such as Amplitude, Mixpanel, Google Analytics, or similar for understanding user behavior and experiment results is highly beneficial.

CRM & Automation:

• While not explicitly mentioned as a primary tool for this role, an understanding of how design experiments impact CRM data and automated workflows would be valuable.

📝 Enhancement Note: Proficiency in Figma is non-negotiable. Experience with specific experimentation and analytics platforms is preferred. The expectation of using AI tools is a significant aspect of the technology stack for this role.

👥 Team Culture & Values

Operations Values:

• Customer-Centricity: Decisions are driven by customer insights and the goal of improving customer value and experience, even within revenue optimization.

• Data-Driven Decision-Making: A strong reliance on data, analytics, and experimentation to validate hypotheses and inform product direction.

• Bias for Action & Iteration: A fast-paced environment that encourages rapid experimentation, learning, and continuous improvement.

• Collaboration & Transparency: Open communication and close partnership with cross-functional teams to achieve shared goals.

• Innovation & Learning: Embracing new technologies (like AI) and fostering a culture of continuous learning and skill development.

Collaboration Style:

• Cross-Functional Integration: Designers are deeply integrated with Product, Engineering, Data Science, and Analytics, working together throughout the experimentation lifecycle.

• Constructive Challenge: A culture where ideas are openly discussed, debated, and constructively challenged to arrive at the best solutions.

• Shared Ownership: Teams share responsibility for experiment success, from ideation and design through to analysis and impact.

📝 Enhancement Note: The team values a blend of analytical rigor, creative problem-solving, and collaborative execution. The emphasis on data and AI integration suggests a modern, forward-thinking operational culture.

⚡ Challenges & Growth Opportunities

Challenges:

• High Volume of Experiments: Managing and executing a continuous stream of experiments requires excellent prioritization, time management, and process efficiency.

• Balancing Speed and Quality: The need for rapid iteration in a fast-paced environment must be balanced with maintaining high standards of design craft and experimentation rigor.

• Ambiguity Navigation: Consistently transforming loosely defined problems into clear, testable hypotheses requires strong analytical and strategic thinking skills.

• AI Tool Integration: Effectively leveraging and integrating new AI tools into existing workflows while ensuring quality and accountability.

Learning & Development Opportunities:

• Specialized Skill Development: Deepen expertise in growth design, experimentation strategy, conversion rate optimization (CRO), and subscription monetization models.

• AI Design Practices: Become a leader in applying AI to product design and experimentation, developing cutting-edge workflows and strategies.

• Cross-Functional Acumen: Enhance understanding of product management, data science, and engineering processes through close collaboration.

• Mentorship: Opportunity to mentor junior designers and contribute to the growth of the design team's capabilities.

📝 Enhancement Note: This role presents challenges related to pace and complexity but offers significant opportunities for specialized growth in high-demand areas like AI-assisted design and revenue experimentation.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you turned an ambiguous brief into a successful A/B test. What was your process, what hypotheses did you form, and what was the measurable outcome?"

• "How do you approach defining success metrics for a new monetization experiment? What data would you look at?"

• "Walk me through your process for collaborating with data scientists and engineers on an experiment from concept to launch."

• "How have you used AI tools to accelerate your design process for experiments? Can you share a specific example?"

• "Imagine we want to increase trial-to-paid conversion. What are three experiment ideas you would propose, and how would you prioritize them?" Company & Culture Questions:

• "What interests you about working in an AI-native organization focused on revenue experimentation?"

• "How do you handle constructive feedback on your designs, especially when they challenge your initial hypotheses?"

• "Describe your experience working in a remote-first environment. What strategies do you use to stay connected and productive?"

• "How do you ensure your design decisions align with broader business objectives and user needs?" Portfolio Presentation Strategy:

• Structure: Organize your portfolio around key case studies that highlight your experimentation journey. For each case study: Problem -> Hypothesis -> Research/Insights -> Design Iterations -> Collaboration -> Experimentation Setup -> Results -> Learnings/Next Steps.

• Quantify Impact: Dedicate a clear section to the measurable results of your experiments. Use charts or graphs if appropriate.

• Articulate Rationale: Be prepared to explain why you made specific design choices, linking them back to hypotheses and data.

• Showcase Process: Use visuals (Figma screenshots, flow diagrams) to illustrate your design process and iterations.

• AI Integration: Highlight instances where AI tools aided your process and discuss the benefits.

• Conciseness: Practice presenting your case studies within a set timeframe, focusing on the most impactful aspects.

📝 Enhancement Note: Interview preparation should focus on demonstrating a strong understanding of the experimentation lifecycle, data-driven decision-making, and the ability to leverage AI tools. Case studies are paramount, requiring candidates to articulate their process and quantifiable impact.

📌 Application Steps

To apply for this operations position:

• Submit your application through the Jobgether platform, which uses an AI-powered matching process to share your profile with the hiring partner company.

• Portfolio Customization: Tailor your portfolio to specifically highlight 2-3 of your strongest revenue experimentation case studies. Ensure each case clearly demonstrates hypothesis generation, design iteration, cross-functional collaboration, and measurable business impact.

• Resume Optimization: Update your resume to emphasize keywords related to product design, revenue experimentation, A/B testing, growth strategy, monetization, Figma, and AI tools. Quantify your achievements wherever possible.

• Interview Preparation: Practice articulating your design process, experiment methodologies, and data interpretation skills. Prepare to discuss your experience with AI tools and how you approach ambiguity. Rehearse presenting your portfolio case studies concisely.

• Company Research: Research the partner company's product offerings and market position (if publicly available) to understand their business context and potential areas for experimentation. Understand their AI-native approach and how it might influence their product strategy.

⚠️ 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.