Top Data Jobs in Toronto

Support stakeholders by profiling, mapping, and validating financial reconciliation datasets. Build complex SQL queries and Python workflows, and use Snowflake and Databricks to design, transform, and manage large-scale finance data for reporting, reconciliation, and analytics. Ensure data quality, perform root-cause analysis, and support governance and documentation.

Lead applied AI projects from prototype to production, building and validating ML and GenAI models (LLMs, embeddings, RAG, agentic workflows) for malicious-behavior, anomaly, and fraud detection. Advise product and engineering teams, design experiments and evaluation pipelines, translate research into scalable APIs/workflows, and ensure data governance, privacy, and ethical practices.

Own and prioritize the single backlog of data products, translate cross-functional needs into scoped requirements, define data definitions and acceptance criteria, partner with the Data Architect to validate designs, ensure outputs are correct, and drive adoption and business value. Deliver an initial prioritized backlog within 90 days and align stakeholders on sequencing and tradeoffs.

Build and operate governed, reproducible AWS-based data pipelines and analytical products. Stand up managed AWS foundation, deliver top-priority data products, support product teams to follow architecture, and apply disciplined reliability and governance while using low-code only when appropriate.

Design and implement a governed AWS data foundation: reference architecture, layered (medallion) data models, governance (PII, classification, lineage, residency), reusable engineering patterns for ingestion/transform/serve, loose coupling to operational systems, and occasionally build data products hands-on to accelerate delivery.

Build, improve, and deploy fraud and loss detection models using supervised and unsupervised ML, causal inference, optimization, and experimentation. Partner with engineering and operations to productionize models, run experiments, and surface insights to shape fraud strategy and reduce financial crime.

Lead end-to-end analytics for business decisions using data insights and AI/ML. Collaborate with cross-functional teams to build scalable solutions and drive advanced analytics adoption, delivering actionable insights and compelling visualizations.

Partner with the Link product team to analyze data, build statistical and ML models, design and analyze experiments, and produce insights to improve conversion, enable local payment methods, and develop consumer features. Communicate findings, collaborate cross-functionally, and deliver impactful, production-ready analyses and models.

Join a talent network connecting data engineers with VC-backed startups. Build scalable batch and real-time pipelines, data models, warehouses/lakehouses, ensure data quality, support ML pipelines, integrate diverse data sources, and define data architecture and governance.

Join a SignalFire talent network to be matched with VC-backed startups seeking Senior/Staff Data Scientists. Responsibilities include experimental design and analysis, predictive and causal modeling, productionizing models, building metrics and dashboards, advising product and business teams, mentoring other data scientists, and shaping data strategy and tooling at fast-moving startups.

Build and maintain scalable data pipelines, dbt models, and warehouse infrastructure to deliver reliable data for analytics and BI. Collaborate with cross-functional teams to enforce data quality, documentation, and governance; monitor pipeline health, investigate and resolve issues, and support data migration and automation efforts.

Design, build, and maintain core data pipelines and models for Lyft's Safety and Customer Care platform. Scale processing, tune Spark/SQL/MapReduce jobs, implement data quality systems, enable self-service ETL tooling, and collaborate with product, engineering, and analytics teams to deliver reliable reporting and insights.

Architect and lead end-to-end production AI solutions across cloud, edge, robotics, and IIoT. Drive AI strategy, product lifecycle, MLOps, model development, deployment, and optimization. Integrate foundation models, multimodal and agentic AI with robotics, sensors, and digital twins. Ensure security, governance, observability, and operational excellence. Mentor teams, coordinate cross-functional delivery, produce technical/service documentation, and translate innovation into commercial value.

The Data Scientist will develop data labeling strategies, optimize datasets for LLMs, analyze data quality, and collaborate with engineering on data processing pipelines.

Design and own end-to-end data quality systems for managed AI data studies: define rubrics, sampling plans, automated checks, launch gates, drift detection, dashboards, and calibration. Investigate integrity issues, run root-cause analysis, build automation in Python/SQL, and partner with Product, Engineering, and Operations to embed quality controls, train reviewers, and scale repeatable quality playbooks across programs.

Research, develop, and deploy ML/AI solutions that power product features. Collaborate with product, engineering, and client teams to analyze data, build models, operationalize MLOps workflows, and generate insights to improve customer and product outcomes.

Lead advanced analytics and ML projects: perform EDA, feature engineering, statistical modeling, and build/optimize production ML and Generative AI solutions (LLMs, embeddings, RAG). Collaborate with engineering and MLOps to deploy and monitor models, create visualizations and dashboards, translate insights for stakeholders, and mentor junior team members.

Design and architect large-scale distributed Big Data solutions using Java, Spark, and Hadoop/Cloudera. Lead global teams to build high-volume data processing and analytics pipelines, ensure robust testing (JUnit), and apply scripting for automation and data analysis.

Build and maintain responsive Angular UI components, collaborate with UX and backend teams, implement REST APIs and Python backend scripts, integrate basic ML models into data workflows, and participate in the full SDLC within Agile teams while ensuring code quality and performance.

Design, develop, test, and deploy high-performance Apache Spark/Scala data processing applications and ETL pipelines on Cloudera (CDH). Optimize Spark and platform performance, build data pipelines using HDFS, Hive, Impala, HBase, and Kafka, ensure data integrity and security, collaborate with data scientists and analysts, and implement version control and CI/CD for deployments.

Design and architect large-scale distributed big data solutions and ETL pipelines using Java, Scala, and Spark. Build and optimize Spark applications on Cloudera CDH (HDFS, Hive, Impala, HBase, Kafka). Ensure data integrity, troubleshoot performance issues, and implement version control and CI/CD for Spark workloads while collaborating with cross-functional teams.

Senior Java Big Data Developer with 8+ years building applications on Cloudera/Hadoop ecosystems using Spark and MapReduce. Responsible for designing performant Java (1.8+) solutions, data analysis of large datasets, unit testing (JUnit), and collaborating with/managing global technology teams. Preferred skills include Unix/Python scripting and familiarity with Hive, Impala, YARN, and Kafka.

Proactively explore RevenueCat data to identify customer problems and opportunities. Translate ambiguous product questions into analyses and production-grade predictive/descriptive models. Partner with Product, Engineering, and Analytics to shape roadmaps, define experimentation and benchmarking approaches, deploy and iterate on models powering customer-facing features, and communicate insights across technical and non-technical audiences.

Founding Data Scientist for RC Capital responsible for building underwriting, fraud detection, anomaly detection, and lead-qualification models; integrating risk signals into product flows; deploying and monitoring production models; analyzing cross-platform revenue data; and defining DS strategy to scale fintech products.

Responsible for supporting data operations, generating reports, and maintaining data quality through advanced Excel and MS Access skills, along with SQL Server and C#. Opportunity for enhancing processes and CRM development.