Jobs in Canada

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Added 4 days ago
SS
Junior Platform Integration
Soho Square Solutions
Montreal, QC
- Temporary
Location: Montreal, QC (Hybrid - 3 days/week onsite)
Added 4 days ago
S
Graph Data Scientist
Swoon
Toronto, ON
- Contract
Work arrangement: Hybrid, 3 days per week onsite
Added 4 days ago
BR
Robotics Software Developer – ROS2, State Estimation, Perception
BHF Robotics
Markham, ON
- Full-time
Development Skills: Exceptional C++ / Python programming skills with a strong grasp of modern software engineering practices (Git, CI/CD, unit testing, profiling).
Added 5 days ago
SM
RQ11673 – Report Developer – Junior
S M Software Solutions Inc
Toronto, ON
- Internship
Work Arrangement: Onsite – 5 Days per Week
Added 5 days ago
SM
RQ11676 – Software Developer – ETL – Intermediate
S M Software Solutions Inc
Toronto, ON
- Internship
Work Arrangement: Onsite – 5 Days per Week
Added 5 days ago
SM
DevOps Engineer
S M Software Solutions Inc
Mississauga, ON
- Full-time
Python and Linux experience will be considered an added advantage.
Added 5 days ago
SM
RQ11676 – Software Developer – ETL – Intermediate
S M Software Solutions Inc
Toronto, ON
- Internship
Work Arrangement: Onsite – 5 Days per Week
Added 5 days ago
SM
RQ11673 – Report Developer – Junior
S M Software Solutions Inc
Toronto, ON
- Internship
Work Arrangement: Onsite – 5 Days per Week
Added 5 days ago
SM
DevOps Engineer
S M Software Solutions Inc
Mississauga, ON
- Full-time
Python and Linux experience will be considered an added advantage.
Added 5 days ago
SM
RQ11673 – Report Developer – Junior
S M Software Solutions Inc
Toronto, ON
- Internship
Work Arrangement: Onsite – 5 Days per Week
Added 5 days ago
SM
Python Developer (Onsite)
S M Software Solutions Inc
Mississauga, ON
- Full-time
The successful candidate must possess strong coding knowledge and the ability to design, develop, and optimize scalable data engineering solutions
Added 5 days ago
SM
RQ11676 – Software Developer – ETL – Intermediate
S M Software Solutions Inc
Toronto, ON
- Internship
Work Arrangement: Onsite – 5 Days per Week
Added 5 days ago
SM
Python Developer (Onsite)
S M Software Solutions Inc
Mississauga, ON
- Full-time
The successful candidate must possess strong coding knowledge and the ability to design, develop, and optimize scalable data engineering solutions
Added 5 days ago
SM
RQ00241 – Software Developer – ETL – Senior
S M Software Solutions Inc
Toronto, ON
- Internship
Work Arrangement: Hybrid – 3 Days per Week Onsite
Added 5 days ago
SM
Java Developer or Python Developer – Data Lineage & Metadata
S M Software Solutions Inc
Mississauga, ON
- Full-time
Client: CITI – Java Developer or Python Developer (Data Lineage & Metadata)
Added 5 days ago
SM
RQ00245 – Machine Learning Engineer – Senior
S M Software Solutions Inc
Toronto, ON
- Full-time
Work Arrangement: Hybrid – 3 Days per Week Onsite · Onsite Requirement: 3 days per week onsite
Added 5 days ago
SM
RQ00244 – Data Science Developer – Senior
S M Software Solutions Inc
Toronto, ON
- Internship
Work Arrangement: Hybrid – 3 Days per Week Onsite · Onsite Requirement: 3 days per week at the client's Toronto office
Added 5 days ago
SM
RQ00245 – Machine Learning Engineer – Senior
S M Software Solutions Inc
Toronto, ON
- Full-time
Work Arrangement: Hybrid – 3 Days per Week Onsite · Onsite Requirement: 3 days per week onsite
Added 5 days ago
SM
RQ00245 – Machine Learning Engineer – Senior
S M Software Solutions Inc
Toronto, ON
- Full-time
Work Arrangement: Hybrid – 3 Days per Week Onsite · Onsite Requirement: 3 days per week onsite
Added 5 days ago
SM
Java Developer or Python Developer – Data Lineage & Metadata
S M Software Solutions Inc
Mississauga, ON
- Full-time
Client: CITI – Java Developer or Python Developer (Data Lineage & Metadata)
Added 5 days ago
SM
RQ00244 – Data Science Developer – Senior
S M Software Solutions Inc
Toronto, ON
- Internship
Work Arrangement: Hybrid – 3 Days per Week Onsite · Onsite Requirement: 3 days per week at the client's Toronto office
Added 5 days ago
SM
RQ00244 – Data Science Developer – Senior
S M Software Solutions Inc
Toronto, ON
- Internship
Work Arrangement: Hybrid – 3 Days per Week Onsite · Onsite Requirement: 3 days per week at the client's Toronto office
Added 5 days ago
SM
Java Developer or Python Developer – Data Lineage & Metadata
S M Software Solutions Inc
Mississauga, ON
- Full-time
Client: CITI – Java Developer or Python Developer (Data Lineage & Metadata)
Added 5 days ago
SM
RQ00241 – Software Developer – ETL – Senior
S M Software Solutions Inc
Toronto, ON
- Internship
Work Arrangement: Hybrid – 3 Days per Week Onsite
Added 5 days ago
SM
Python Developer (Onsite)
S M Software Solutions Inc
Mississauga, ON
- Full-time
The successful candidate must possess strong coding knowledge and the ability to design, develop, and optimize scalable data engineering solutions

Swoon

Graph Data Scientist

Toronto, ON, Canada

Hybrid

Contract

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Job details

Job type: Contract
Workplace: Hybrid
Apply by: Dec 8, 2026
Posting language: English
Posted: Nov 8, 2026

Original job description

Graph Data Scientist

6-Month Contract with Potential For Extension and Conversion

Hybrid - 3 days/week in office

About the Opportunity

We’re seeking a Graph Data Scientist to help develop advanced AI and machine learning solutions for a strategic client network project. This role focuses on uncovering meaningful patterns, relationships, communities, and predictive insights from large, highly connected datasets that traditional analytics methods may not fully capture.

You’ll work at the intersection of graph machine learning, knowledge graphs, deep learning, and scalable AI, translating complex network data into actionable business intelligence and production-ready solutions.

This is an opportunity to take ownership of graph data science initiatives, experiment with emerging techniques, and collaborate with data engineering, platform, product, and business stakeholders to bring innovative analytical solutions into production.

Contract Details

Duration: 6 months, with potential for extension and conversion
Work arrangement: Hybrid, 3 days per week onsite
Locations: Toronto, ON; Raleigh, NC; New Jersey; New York; or Jacksonville, FL
Interview process: One virtual interview followed by one onsite interview

What You'll Do

Graph Machine Learning and Advanced Analytics

Design and implement graph-based machine learning solutions, including node classification, link prediction, community detection, centrality analysis, similarity scoring, and anomaly detection.
Develop graph embeddings and representation-learning models using techniques such as Node2Vec, GraphSAGE, graph neural networks (GNNs), and transformer-based approaches.
Identify hidden relationships, influence patterns, communities, risks, and predictive signals across complex networks.
Build and evaluate models for use cases such as recommendations, relationship discovery, fraud or risk detection, and network optimization, depending on business requirements.
Apply graph theory and network analysis to translate ambiguous business challenges into measurable analytical outcomes.

Knowledge Graphs and Data Engineering

Design and enhance knowledge graphs by defining entities, relationships, ontologies, taxonomies, identity-resolution rules, and data provenance.
Query and analyze graph data using Cypher, Gremlin, SPARQL, or equivalent graph query languages.
Work with graph platforms such as Neo4j, TigerGraph, Amazon Neptune, or ArangoDB.
Partner with data engineers to develop scalable data ingestion, entity-resolution, feature-engineering, and graph-processing pipelines using Python, SQL, Spark, Databricks, or similar technologies.

Model Development and Production Deployment

Build, train, tune, and evaluate deep learning models using PyTorch or TensorFlow, including graph neural networks and transformer architectures.
Establish robust evaluation practices covering model performance, baseline comparisons, error analysis, explainability, bias, and business impact.
Help transition prototypes and experiments into production through APIs, Docker, Kubernetes, CI/CD, model registries, and monitoring.
Support reproducible ML workflows and production-ready solutions across AWS, Azure, or Google Cloud environments.

Collaboration and Technical Communication

Collaborate with engineering, product, platform, and business teams to move graph analytics from research and experimentation into practical applications.
Present findings, visualizations, model limitations, and recommendations to technical and non-technical stakeholders.
Document methodologies, assumptions, evaluation criteria, and model performance.
Stay current with developments in graph AI, knowledge graphs, NLP, large language models, GraphRAG, and responsible AI.

What We're Looking For

Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Applied Mathematics, Engineering, or a related quantitative discipline. A Ph.D. is an asset.
At least 2 years of relevant professional experience in data science or machine learning, including meaningful hands-on experience with graph analytics, graph machine learning, or knowledge graphs.
Strong understanding of graph theory and algorithms, including PageRank, shortest paths, connected components, centrality, community detection, similarity, and link prediction.
Experience with at least one graph database or platform, such as Neo4j, TigerGraph, Amazon Neptune, or ArangoDB, and its associated query language.
Advanced Python skills, including pandas, NumPy, scikit-learn, network analysis libraries, and sound software development practices.
Hands-on experience developing and tuning deep learning models using PyTorch or TensorFlow.
Familiarity with graph embeddings and representation learning, such as Node2Vec, Word2Vec, BERT-family models, or sentence embeddings.
Strong SQL skills and experience working with large datasets using Apache Spark, Databricks, Hive, Presto, Trino, or comparable technologies.
Experience deploying machine learning workloads in a cloud environment, ideally with Docker and Kubernetes.
Strong analytical problem-solving skills, with the ability to validate assumptions, interpret model behavior, and turn complex findings into actionable recommendations.
Excellent communication skills and experience working with cross-functional teams and business stakeholders.

Nice to Have

Production experience with graph neural networks and tools such as PyTorch Geometric, Deep Graph Library (DGL), Neo4j Graph Data Science, or TigerGraph Machine Learning Workbench.
Experience designing enterprise knowledge graphs, ontologies, RDF/OWL models, semantic layers, SPARQL endpoints, or entity-resolution frameworks.
Exposure to large language models, NLP, retrieval-augmented generation (RAG), or GraphRAG.
Knowledge of recommendation systems, network optimization, advanced anomaly detection, reinforcement learning, or time-series forecasting.
Familiarity with end-to-end MLOps, experiment tracking, feature management, model registries, automated testing, drift monitoring, and model retraining.
Experience with Git, pull requests, CI/CD, infrastructure automation, and secure cloud engineering.
Background in consulting or network-intensive industries such as financial services, telecommunications, cybersecurity, or healthcare.

What Success Looks Like

Developing accurate, explainable, and scalable graph models that address defined business needs.
Improving the discovery of relationships, communities, recommendations, risks, or anomalies within complex datasets.
Building reproducible and well-documented data and model pipelines.
Helping stakeholders understand analytical findings and translating successful prototypes into production-ready solutions.

Technology Environment

Graph Databases and Knowledge Graphs: Neo4j, TigerGraph, Amazon Neptune, ArangoDB, Cypher, Gremlin, SPARQL

Graph ML and Deep Learning: PyTorch, TensorFlow, PyTorch Geometric, DGL, GraphSAGE, Node2Vec, Transformers

Data Science and Distributed Processing: Python, SQL, pandas, NumPy, scikit-learn, Apache Spark, Databricks, Hive, Presto, Trino

Cloud and MLOps: AWS, Azure, GCP, Docker, Kubernetes, CI/CD, experiment tracking, model monitoring

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More about this job

Responsibilities

Design and develop graph machine learning models and knowledge graphs to identify relationships, communities, recommendations, risks, and anomalies in complex datasets. Build scalable data and model pipelines, help deploy solutions into production, and communicate findings and recommendations to technical and business stakeholders.

Requirements

Requires a bachelor's or master's degree in a quantitative discipline and at least two years of relevant data science or machine learning experience, including hands-on graph analytics, graph machine learning, or knowledge graphs. Candidates should have strong Python, SQL, graph theory, graph database, and deep learning skills, plus experience with large datasets, cloud deployment, and cross-functional communication.

Skills

Graph Machine Learning
Graph Theory
Knowledge Graphs
Python
PyTorch
TensorFlow
Graph Neural Networks
Graph Databases
Cypher
SQL
Apache Spark
Deep Learning
Cloud Computing
Docker
Kubernetes
MLOps

Visa sponsorship

Not detected in the job text

Keywords

Graph Machine Learning
Graph Theory
Knowledge Graphs
Graph Neural Networks
Node2Vec
GraphSAGE
Transformers
PyTorch
TensorFlow
PyTorch Geometric
Deep Graph Library
Neo4j
TigerGraph
Amazon Neptune
ArangoDB
Cypher
Gremlin
SPARQL
Python
SQL
Apache Spark
Databricks
AWS
Azure
Google Cloud
Docker
Kubernetes
CI/CD
MLOps
Entity Resolution

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Jobs in Canada