Top Tech Jobs & Startup Jobs in Toronto

Leads Wave’s financial services product strategy across payments, platform modernization, fraud and risk mitigation, and future SMB financial capabilities. Oversees product managers, platform reliability, compliance, customer trust, partnerships, budgets, OKRs, and cross-functional execution. Coaches teams, improves product development practices, and identifies effective AI applications for productivity, fraud detection, risk decisions, operations, and customer experience.

Lead strategy and execution for Core Services shared-platform products. Own roadmap, orchestration, APIs, adoption, platform health metrics, and a team of Product Managers while partnering with Engineering, Data, and Design to scale reusable services and improve developer velocity.

Conduct product, marketing, regulatory, and partner compliance reviews; research US and Canadian requirements; assess risks; track remediation and approval conditions; maintain regulatory inventories, policies, issue logs, calendars, and compliance metrics; assemble audit evidence; support policies and training; and identify opportunities to automate compliance processes.

Lead the Chief Compliance Officer’s effort to scale Wave’s compliance program. Build regulatory inventories, policies, risk assessments, issue management, and regulatory change processes. Run product compliance reviews, oversee partner and processor due diligence, coordinate audits, monitor regulatory obligations, and report risks and KPIs to leadership. The role partners closely with Product, Engineering, Legal, and Risk teams and requires expertise in fintech, payments regulation, and compliance program development.

Own day-to-day operations and change delivery for an assigned financial product line. Translate product requirements into execution plans, lead launches, testing, readiness, documentation, monitoring, controls, reconciliations, and issue remediation. Partner with product, operations, and technology teams to improve workflows and automation, support audits, resolve operational issues, and provide leadership updates. The role requires financial services experience, product operations expertise, analytical skills, and proficiency in SQL or Power BI.

Lead creative development for integrated campaigns, product launches, brand initiatives, websites, paid media, email, social, video, and motion graphics. Develop visual storytelling, evolve brand identity, create conversion-focused digital experiences, and use performance data and experimentation to improve results. Build reusable templates and asset libraries, apply AI-assisted workflows, manage projects from concept through production, and collaborate with marketing, product, content, web, agencies, and freelancers.

Build and maintain scalable financial products in a cross-functional agile team. Responsibilities include writing tested code, solving cross-system challenges, owning initiatives from definition through delivery, creating technical plans and documentation, supporting production monitoring, improving performance and observability, collaborating across teams, and mentoring engineers. The role requires backend development with Python and Django, frontend development with React and TypeScript, financial API integrations, data modeling, staged rollouts, and occasional after-hours on-call support.

Owns chargeback disputes from merchant notification through resolution, including evidence collection, case submission, and adherence to card-network rules. Coaches merchants on dispute prevention, collaborates with Risk, Finance, Product, banks, and vendors, and identifies process improvements. The role also supports fraud prevention, reporting, collections, accounts receivable, customer communications, and analysis to improve chargeback efficiency and reduce losses.

Analyzes fraud patterns, transaction activity, payment flows, model outputs, and risk signals to identify threats and control gaps. Designs and tunes fraud rules, scoring strategies, alerts, and decisioning controls; monitors fraud losses, capture rates, false positives, and operational metrics. Partners with Product, Engineering, Data Science, Compliance, and Operations to implement fraud improvements, evaluate tools, conduct root-cause analysis, and provide actionable recommendations and executive-ready reporting.

Design, build, train, and deploy production ML models and scalable pipelines. Implement MLOps practices, observability, governance, and lineage. Optimize model performance and costs, collaborate with product, risk, and engineering teams to integrate ML features into production systems.