Senior Data Scientist

Job Description

<p><strong>Job Title: Senior Data Scientist</strong></p><p><strong>Location:</strong> Toronto</p><p><strong>Department: </strong>AI & Data Engineering</p><p><strong>Type: </strong>Full-time</p><p><strong>Reports to: </strong>Head of Applied AI & Data Engineering</p><p><br></p><p><strong>Who is EnStream</strong></p><p><br></p><p>EnStream is a leader in secure digital identity and mobile data intelligence, working to advance the future of digital trust in Canada. We build innovative data-driven models that enhance the integrity, reliability, and safety of digital identity ecosystems. Our latest initiative leverages <strong>advanced data science</strong>, <strong>machine learning</strong>, and <strong>deep learning </strong>to further <strong>grow and sustain digital trust </strong>across Canada.</p><p><br></p><p>Our mission is to <strong>empower frictionless trust in every interaction. </strong>EnStream is dedicated to increasing trust and convenience for Canadians using real-life, verified identities and network data held by trusted telco networks. At EnStream, every team member plays a critical role in shaping our strategy and delivering meaningful impact across industries.</p><p><br></p><p><strong>About the Role</strong></p><p><br></p><p>We are seeking a <strong>Senior Data Scientist </strong>to join EnStream's <strong>Data & AI team </strong>and help design and build models that measure behavioural, relational and contextual integrity across the EnStream ecosystem. You will apply your expertise in <strong>data wrangling, feature engineering, machine learning, and deep learning</strong>-with a strong preference for experience in <strong>graph-based </strong>and <strong>semi-supervised learning</strong>-to develop models that are <strong>explainable, scalable, and production-ready</strong>. Additionally, you will conduct ad-hoc statistical analysis to support production operations and future initiatives.</p><p><br></p><p><strong>What You'll Do</strong></p><p><br></p><ul><li>Research, design, and prototype <strong>Machine Learning/Deep Learning models </strong>for identity trust and integrity scoring</li><li>Prepare, clean, and engineer features from large, complex <strong>telecommunications and fraud datasets</strong></li><li>Develop and evaluate <strong>unsupervised and semi-supervised learning </strong>models (including graph-based techniques)</li><li>Collaborate with data and application engineering teams to <strong>operationalize data engineer pipeline and AI/ML models</strong></li><li>Support <strong>ad-hoc statistical analysis, data visualization</strong>, and insight generation for exploratory studies and impact assessments</li><li>Contribute to the design of <strong>model monitoring</strong>, explainability, and drift detection frameworks</li><li>Participate in peer reviews, documentation, and knowledge sharing within the Data & AI team</li></ul><p><br></p><p><strong>What You Bring</strong></p><p><br></p><p><strong>Must-Have Skills & Experience</strong></p><p><br></p><ul><li>Bachelor's or higher degree in <strong>Computer Science, Data Science, Engineering, Mathematics</strong>, or related field</li><li>Domain knowledge in <strong>fraud detection</strong></li><li><strong>5+ years </strong>of hands-on experience in <strong>Data Science, Machine Learning, Deep Learning</strong></li><li>Proficiency in Python (e.g. numpy, pandas, PySpark, scikit-learn, PyTorch/TensorFlow, matplotlib, seaborn), SQL, hyperparameter optimization framework (e.g., Ray Tune, Optuna, Hyperopt), and graph ML frameworks (e.g., PyTorch Geometric, NetworkX)</li></ul><p><br></p><p><strong>What Sets You Apart</strong></p><p><br></p><ul><li>Domain knowledge in <strong>digital identity </strong>and <strong>telecommunications</strong></li><li>Experience with advanced <strong>Unsupervised </strong>and <strong>Semi-Supervised </strong>Learning techniques</li><li>Experience in Data Engineering or ML Engineering</li><li>Experience with AWS S3, SageMaker, and lakehouse architecture</li><li>Experience implementing model monitoring, data/concept drift detection and explainability frameworks (e.g. SHAP, LIME)</li></ul><p><br></p><p><strong>Why Join Us?</strong></p><p><br></p><ul><li>Contribute to a <strong>national-scale initiative </strong>defining the future of <strong>digital trust </strong>in Canada</li><li>Work on cutting-edge <strong>graph-based semi-supervised learning </strong>applications using real-world identity data</li><li>Collaborate with a highly skilled, cross-functional team</li></ul><p><br></p>