Top Python Jobs in Toronto

Analyze player and business data to improve engagement, retention, and monetization. Build models, forecasts, dashboards, and A/B tests; synthesize qualitative feedback; partner with cross-functional teams; ensure tracking accuracy; and leverage AI tools to accelerate insights.

Design and build platforms, services, and pipelines to train, host, integrate, and monitor machine learning models; collaborate with data scientists; gather and process large datasets; implement algorithms and distributed/data infrastructure.

Build and maintain production-grade data processing systems and foundational infrastructure for enterprise AI. Collaborate with researchers and engineers, deliver end-to-end implementations, transform unstructured data into performant datasets, and use distributed processing frameworks and modern analytics tooling to inform products and strategy.

Develop data-analysis pipelines bridging ultrasound, synthetic biology, imaging and sequencing. Collaborate with wet-lab and ML teams to define objectives, encode priors, run ML pipelines for de-novo design and closed-loop active learning, and produce actionable insights to accelerate molecule discovery and device optimization.

Lead architecture, design, validation, and productization of mixed-signal electronics and instrumentation for high-bandwidth brain interfaces. Own PCB/system architecture, SI/PI, EMC/EMI, power, clocks/triggers, automated test and manufacturing transfer. Build reusable scientist-facing measurement hardware, define quantitative performance criteria, collaborate cross-functionally, manage vendors/CMs, and mentor engineers to establish EE best practices.

The Software Engineer will lead the development of Baseten Voice AI, focusing on real-time model serving systems, collaboratng with teams on architecture and implementation.

Design, develop, and deploy machine learning and NLP solutions at scale. Work with cross-functional teams to build content classification, representation learning, search, and recommendation models using classical ML, deep learning, and LLMs. Process large datasets with Python, SQL, and Spark, and communicate results to stakeholders with clear documentation.

Lead architecture and delivery of scalable AI systems for P&C insurance, oversee fraud prevention platform from data to production, mentor AI engineers, collaborate with business stakeholders, prototype novel AI techniques (LLMs, GNNs, CV), and drive cloud deployments and responsible AI practices.

Design, build, and maintain high-performance data pipelines on Snowflake using dbt and AWS. Own code (SQL, Python, Shell, Terraform), ensure data quality and governance, troubleshoot complex issues, lead agile delivery and mentor engineers, collaborate with architects and QA, and produce technical documentation.

Senior individual-contributor analytics role partnering with Sales and Partnerships to drive acquisition decisions. Lead funnel and channel analyses, experimentation, forecasting, impact evaluation, KPI governance, and scalable analytics. Translate ambiguous business questions into actionable recommendations, mentor analysts, build dashboards and decision tools, and collaborate with BI, Data Science, and cross-functional stakeholders to improve acquisition quality and efficiency.

Lead and grow a Sales & Marketing analytics team to build measurement infrastructure linking acquisition investment to subscription revenue. Own advanced marketing measurement (MMM, MTA, incrementality, experimentation), funnel and sales analytics, dashboards, and decision-support tools. Partner with cross-functional stakeholders and data teams to improve CAC, LTV, payback, and go-to-market ROI while advancing AI-enabled, self-serve analytics and automation to scale trusted, executive-ready insights.

Lead commercial pricing initiatives and develop rate change projects and advanced analytical models. Collaborate with business partners, communicate actuarial insights, mentor team members, and manage medium-to-large projects to ensure portfolio profitability.

Plan and execute a zero-downtime migration of stateful data systems (Druid, Spark, Redpanda, Airflow, PostgreSQL, Elasticsearch) from bare-metal VMs to Kubernetes on OpenStack. Codify infrastructure as code, implement safe StatefulSet/PVC and upgrade patterns, and own day-to-day operational health. Build observability, SLOs, runbooks, KPIs, and optimizations. Partner with SRE on hardware, networking, and Kubernetes fundamentals while owning data applications end-to-end.

Lead technical analysis and design for data solutions, create data models and mapping documents, identify input/output data, mentor junior analysts, and support data warehousing, ETL, and cloud-based implementations.

Design, develop, test, and support data and AI products. Partner with stakeholders to translate requirements, build ETL/ELT pipelines, perform data modeling/profiling, automate frameworks, troubleshoot production issues, and participate in process improvements.

Design and deliver AI Agent features for Veeva Labeling, build prototypes and production ML integrations, define data flows and prompts, collaborate with engineering and UX, evaluate AI agents, and present capabilities to stakeholders.

Manage pricing across product portfolios through data analysis, weekly pricing reviews, and pricing overrides. Identify pricing levers, model impacts on profitability and volume, ensure pricing system accuracy, collaborate with stakeholders, and present actionable recommendations to improve revenue and margins.

Own a pod's roadmap and delivery end-to-end: write problem-first specs, participate in code and debugging, own QA and release sign-off, monitor post-deploy via Sentry and Grafana, close feedback loops between customers, data, and product KPIs, and drive fast, async-first decision making.

Build and maintain automated publishing systems that deploy SEO-optimized content across WordPress, Webflow, Shopify, and custom CMSs using Claude Code. Develop API integration layers, scripting, and QA pipelines to prevent bad content from reaching production. Coordinate with content and fulfillment teams, document systems, run staging tests, and validate AI model changes to ensure automated content performs and publishes reliably.

Own end-to-end product features: design, build, ship, and iterate. Lead Angular front-end architecture and collaborate with backend teams (Go/Node/Python). Use AI-assisted development tools daily, instrument and analyze feature usage, run experiments, and mentor engineers while driving engineering excellence and cross-functional product decisions.

Design and build distributed backend systems, microservices, and near-real-time data ingestion and processing. Scale an incrementality experimentation platform, add AI-powered workflows, extend data integrations and partner exports, and contribute frontend components as needed to deliver end-to-end product features.

Design, build, and deploy production-grade LLM systems (RAG, agents, APIs). Own end-to-end delivery, define evaluation frameworks, and optimize for cost, latency, and reliability while providing technical leadership and cross-team collaboration.

Serve as technical bridge for customer onboarding and integrations: maintain/debug internal admin tools, configure feature flags, ensure PMS/CRM API integrations, update docs, troubleshoot regressions and basic AI hallucinations, and collaborate with engineering to automate onboarding/support workflows.

Lead architecture and implementation of agentic AI systems powering Zuma's platform. Rebuild onboarding and integration frameworks, implement analytics and observability for LLM performance, translate product needs into technical solutions, mentor engineers, establish engineering standards, and work directly with customers to enable scalable, production-grade AI experiences.

Lead end-to-end development and production deployment of agentic AI systems and full-stack products. Architect integrations with property management systems, improve reliability and observability, define engineering standards, and partner with product and operations to ship scalable AI agents across voice, text, and email.