Top Python Jobs in Toronto

Lead Data Scientist analyzing global fraud data to identify patterns, develop predictive models, and prevent payment fraud. The role builds fraud-related products and services, extracts insights from large datasets, collaborates with stakeholders, and presents findings to management and clients. Required skills include Python, Spark, complex data queries, and advanced data analysis. Experience with agentic AI systems and Unix is preferred, along with payments or fraud expertise.

Supports sales, customer success, and product development for Mastercard Identity through data analysis, machine learning, rules-based fraud models, and proof-of-value testing. Advises customers on fraud workflows, analyzes data features, supports API integration QA, and provides product recommendations. Builds customer relationships, translates business needs into technical solutions, contributes to product specifications and training, and serves as a subject matter expert on identity data and products.

Leads technical go-to-market activities for enterprise customers, including discovery, architecture design, technical validation, demonstrations, proofs of concept, onboarding oversight, adoption, and expansion. Acts as a trusted technical advisor across the customer lifecycle while carrying revenue quota. Designs migration and deployment strategies, resolves technical objections, leads executive reviews, automates workflows with AI, and develops technical enablement resources.

Leads technical go-to-market activities for enterprise customers, including discovery, architecture design, technical validation, demonstrations, onboarding oversight, adoption, QBRs, and account expansion. The role serves as a trusted technical advisor across the customer lifecycle, translates technical solutions into business value, supports network and security deployments, identifies risks, and uses AI automation to streamline workflows. It carries revenue accountability and requires strong enterprise architecture, communication, and consultative selling skills.

Serve as a quota-carrying technical advisor for enterprise customers throughout pre-sales validation, onboarding, deployment architecture, adoption, renewal, and expansion. Design Cloudflare network and security solutions, lead technical discovery, demonstrations, proofs of concept, migration blueprints, QBRs, and executive engagements. Translate technical capabilities into business value, identify risks and expansion opportunities, and automate workflows using AI, LLMs, scripting, and internal platforms. Travel up to 20–50% may be required.

Build and scale backend-focused software for Braze’s petabyte-scale Data Lakehouse. Develop data pipelines, asynchronous processing, ETL applications, and end-to-end features across platforms such as Snowflake, ClickHouse, and Databricks. Drive architectural decisions, contribute to technical strategy, and deliver scalable web applications handling high volumes of data and traffic in an agile environment.

Build and maintain backend-focused, end-to-end software for Braze’s petabyte-scale Data Lakehouse platform. Develop infrastructure and tools supporting Snowflake, ClickHouse, and Databricks, manage high-volume event data, and deliver front-end experiences when needed. Collaborate across teams, apply strong software design and algorithms knowledge, and help scale systems handling billions of daily rows and significant customer and internal workloads.

Serves as a quota-carrying technical advisor for enterprise customers throughout the full sales and adoption lifecycle. Leads technical discovery, architecture design, proofs of concept, presentations, onboarding blueprints, deployment guidance, QBRs, and expansion strategy. Partners with account executives and customer engineering teams to translate Cloudflare’s networking, security, performance, and developer platform capabilities into business outcomes. Uses AI and automation to streamline technical workflows, while creating enablement materials and managing integration risks.

Architect, build, and optimize BuildOps’ full-stack platform using React.js, Node.js, GraphQL, REST, and SQL. Lead technical decisions, API and database optimization, observability, testing, cloud infrastructure, and event-driven architecture. Mentor engineers, establish engineering best practices, guide cross-team technical initiatives, and collaborate with product, QA, customers, and partners to deliver reliable, scalable software.

Lead technical presales engagements for HPE Mist campus and branch networking solutions. Partner with sales teams on discovery, architecture, demonstrations, proof-of-concept projects, competitive positioning, and customer adoption. Configure and troubleshoot wireless, wired, WAN, access control, cloud management, automation, and AI-driven networking solutions. Advise executives and technical stakeholders, enable partners and internal teams, and coordinate with Customer Success, Support, Product Management, and Engineering. Travel may be required up to 50%.

Test and validate AI models and software for automotive use cases: generate ground truth, build annotated datasets and testing frameworks, evaluate technologies, support R&D engineers, coordinate projects and outsourced work, and collaborate with internal and external partners to drive innovation and product readiness.

Design, develop, deploy, and maintain full-stack agentic AI applications from concept through production. Responsibilities include agent orchestration, data and tool integration, MCP server connectivity, frontend UI/UX development, cloud deployment, engineering-process analysis, technology evaluation, testing, validation, documentation, and innovation project coordination. The role collaborates with R&D teams and internal customers to create reliable AI solutions that improve efficiency, quality, and scalability.

Leads the technical design and development of fault-tolerant, cross-functional full-stack applications for Capital One’s travel portal. Builds front-end and back-end systems, contributes to architecture and high-level design, establishes engineering standards, improves developer tools and experience, provides technical support, and mentors junior and intermediate engineers. Collaborates throughout the software development lifecycle in an agile, hybrid work environment.

Backend software engineering intern responsible for developing and scaling high-volume APIs, ETL pipelines, and production backend services. The role includes writing Java, TypeScript, Python, or other backend code; building automated tests; improving CI/CD deployment pipelines; working with cloud infrastructure and databases; participating in code reviews and Agile ceremonies; and collaborating with product managers, architects, and engineers under senior mentorship.

Analyze player behavior, product performance, revenue, retention, and engagement for Zynga Poker. Design experiments, dashboards, ETL pipelines, telemetry, fraud models, and business intelligence solutions. Partner with product, game design, economy, and consumer insights teams to guide decisions and improve player experiences. Contribute to AI-enabled analytics workflows, game integrity initiatives, and analytics best practices.

Design and build cloud-native microservices, REST APIs, and highly available backend services supporting large-scale data requests. Translate business requirements into scalable platform solutions, contribute to architecture modernization, and uphold engineering practices including testing, observability, CI/CD, and code reviews. Troubleshoot production issues, improve reliability, and evaluate emerging data engineering and generative AI technologies. Mentor colleagues and collaborate across technical and business teams.

Build and improve test infrastructure, automation frameworks, CI/CD pipelines, and end-to-end testing strategies. Lead quality initiatives, develop AI-augmented testing solutions, monitor test environments, support incident resolution, and use quality and performance metrics to improve software reliability. Collaborate with Infrastructure, SRE, engineering leaders, and product teams in an agile environment to expand test coverage and enable safe, efficient delivery.

Build and operate scalable AI platform services, internal APIs, and production workflows integrating LLMs and AI tools. Own reliability through observability, alerting, incident response, performance tuning, cost optimization, capacity planning, CI/CD, and infrastructure improvements. The role requires Python backend development, distributed systems, asynchronous processing, cloud infrastructure, infrastructure as code, and cross-layer experience spanning services, data, infrastructure, and AI tooling.

Owns analytics and optimization strategy for advertising revenue across Zynga’s mobile game portfolio. Responsibilities include monitoring performance, diagnosing trends, designing A/B tests, identifying monetization opportunities, partnering with product, analytics, and data science teams, supporting AI and ML models, leading cross-functional initiatives, and mentoring analysts.

Own production database troubleshooting, incident root-cause analysis, performance tuning, observability, and automation across large-scale multi-tenant PostgreSQL and MariaDB environments. Analyze Linux and Unix performance, support distributed web applications, conduct stress testing, influence cloud infrastructure decisions, mentor engineers, and develop mission-critical software. The role requires extensive database management, cloud infrastructure, concurrency, multithreading, scripting, and distributed-systems experience.

Lead pricing modeling and experimentation to optimize margin, conversion, and retention. Build elasticity and willingness-to-pay models, run causal and bandit experiments, decompose merchant economics, and develop agentic deal tooling. Own end-to-end data pipelines, ETL, monitoring, and visualization. Evaluate AI systems for accuracy, bias, and drift, and partner with cross-functional teams to translate results into pricing strategy and automation.

Build and operate applied AI systems, including LLM integrations, agentic workflows, data pipelines, evaluation frameworks, MCP tools, APIs, and governed content-serving layers. The role emphasizes production reliability, provenance, security, cost and latency management, regression detection, and human-reviewed AI outputs. Responsibilities include cloud deployment, structured content modeling, telemetry, governance, mentoring, and collaboration on AI standards. Strong Python, TypeScript, AWS, API, data pipeline, and production LLM experience are required.

Build and operate production applied AI systems, including LLM integrations, agentic workflows, data pipelines, evaluation frameworks, APIs, MCP tools, and governed content-serving layers. Develop reliable extraction and classification agents, model evaluation harnesses, telemetry, and structured data pipelines. Ensure security, privacy, provenance, reliability, latency, and cost controls while mentoring engineers and contributing to AI governance and standards.

Lead technical strategy and execution for foundational Trust and Safety backend systems supporting consumer risk and credit reporting. Design and operate highly available distributed systems, improve reliability, monitoring, testing, and on-call processes, and guide cross-functional initiatives. Establish engineering standards, conduct technical planning, mentor engineers, communicate architectural decisions, and drive system quality, scalability, and sustainability across teams.

Build and operate production MongoDB integrations and application capabilities directly within ISV codebases. Design data models, migrations, synchronization, dual-write and CDC solutions, and production-ready deployments across cloud, Kubernetes, and on-premises environments. Collaborate with partner engineers through implementation, code reviews, documentation, rollout, and knowledge transfer. The role involves regular travel and on-site work with partners.