Senior Product Data Analyst

Role overview

This is a senior analytics position for a Canada-based candidate, supporting a partner company that handles the entire application and hiring flow. The role sits at the crossroads of product strategy, decision science, and business performance, with a strong focus on influencing important company decisions through data.

You will work closely with Product, Engineering, Finance, and go-to-market leadership as a strategic advisor. The role is centered on solving complex business questions around pricing, conversion, risk, and profitability in financial products. Instead of only responding to requests, you will help frame the problem, choose the right analytical approach, and guide the team toward practical decisions.

Your analysis will shape product direction, roadmap priorities, and broader decision-making across the organization. The environment is fast-moving, experimentation-led, and highly data driven, offering strong visibility and the chance to influence core financial product strategy at scale.

Key accountabilities

• Work alongside Product, Engineering, Finance, and GTM teams to turn business challenges into structured analytical work and data-backed decision frameworks.
• Own analytics projects from start to finish, including problem framing, modeling, recommendations, and implementation support, while keeping outcomes tied to business goals.
• Create and apply analytical models that help balance pricing, risk, conversion, profitability, and customer experience.
• Plan, run, and assess experiments such as A/B tests and pricing or conversion optimization studies.
• Surface product opportunities, operational gaps, and strategic insights from large transactional and behavioral data sources.
• Deliver clear, executive-level recommendations that shape roadmap choices and strategic priorities.

Requirements

• Proven background in product analytics, decision science, or data science within a product-led or fintech setting.
• Advanced SQL capability and confidence working with large transactional or product-level datasets.
• Strong Python skills for statistical analysis, experimentation, modeling, or exploratory data work.
• Hands-on experience designing experiments and building frameworks that inform product and business decisions.
• Ability to turn unclear business problems into structured analysis and actionable recommendations.
• Strong communication and stakeholder management skills, with the credibility to influence senior Product and Engineering leaders.
• Experience using BI and visualization tools such as Looker, Tableau, or comparable platforms.

Benefits and perks

• Flexible working setup with autonomy and support for work-life balance.
• Competitive pay plus equity participation opportunities.
• Health, dental, and wellness coverage.
• Pension/RRSP contributions for long-term financial planning.
• Flexible paid time off and supportive parental leave policies.
• Learning, training, and professional development support.
• Wellness and health credits for physical and mental well-being.
• Volunteer time and community involvement initiatives.
• Employee social groups, interest clubs, and team activities.
• Equipment and home-office support to improve the remote work setup.

Application and hiring process

This job is listed through a partner-led process. Applications and next steps are handled by the partner company, and shortlisted candidates are then passed along to the hiring employer. Initial screening uses an automated matching approach to compare applicants against the role’s core requirements, followed by human review for final hiring decisions.

Privacy and data processing

By applying, you acknowledge that your personal data may be processed to assess your candidacy and shared with the hiring employer where relevant. This processing is based on legitimate interest and pre-contractual measures under applicable privacy laws, including GDPR. You may request access, correction, deletion, or objection at any time.

Automated tools may be used to support parts of the recruitment process, including application review, resume analysis, response assessment, and detection of possible inconsistencies or verification signals. These tools assist the recruiting team but do not replace human judgment. Final hiring decisions are made by people.