Our healthcare client is seeking a Lead Data Scientist to bridge the gap between business needs and advanced analytical solutions
This is a full time permanent position. Hybrid work model 1-2 times a month on site in Markham, ON. Open to fully remote if located outside of the GTA.
The successful candidate will own the end-to-end analytics lifecycle - from understanding complex healthcare workflows to deploying data science and machine learning models in production. This position requires proficiency in stakeholder management and technical implementation, leading both the discovery of opportunities and the delivery of solutions. The scope of work includes: stakeholder management, requirements gathering, leading workshops, end-to-end Data Science and Machine Learning (ML) accountability, data discovery and EDA, creation of compelling data visualizations/reporting, deployment and testing
Must Haves:
Data Science & Machine Learning Python programming (
NumPy, Pandas, Scikit-learn, TensorFlow/PyTorch) Experience
leading discovery shops is mandatory (not just getting requirements from BAs) Strong statistical knowledge and experimental design Experience with
Azure ML or similar cloud ML platforms is a strong asset Model lifecycle management experience
Python, Pandas, GeoPandas Analytics & BI Understanding General knowledge of modern
BI/analytics platforms Experience with SQL and data manipulation Proven track record and extensive experience leading requirements gathering for complex analytical projects Proven track record of translating business needs to technical solutions Experience with process mapping and workflow analysis
Key Responsibilities:
1. Data Science & Machine Learning Leadership
Technical Development
Guide and mentor exploratory data analysis (EDA) and feature engineering efforts Design, develop/code, and validate machine learning models Conduct advanced statistical analysis to derive model selection and training Model Development: Lead end-to-end ML project development including EDA, feature engineering, model selection, training, and validation Azure ML Implementation: Oversee design and implementation of ML pipelines using Azure ML, including model deployment, monitoring, and retraining Statistical Analysis: Conduct advanced statistical analysis, hypothesis testing, and model validation using appropriate methodologies
Technical Team Leadership
Project Management: Lead cross-functional ML projects from conception through deployment and monitoring Peer Review: Conduct technical reviews of ML models, code quality, and deployment strategies Lead and mentor data scientists and analysts Establish technical standards for ML development Oversee and Collaborate with data engineers on ML pipeline design Identify and help prioritize machine learning use cases across the organization Champion adoption of predictive analytics in operations – this includes presenting results, solutions and their application
GoTool Platform Involvement
Manage and evolve the GoTool AI/MLOps platform (our in-house AI/ML platform) Ensure platform reliability and performance Drive platform enhancements based on user needs Manage quarterly model refreshes and updates Coordinate with stakeholders on platform roadmap
2. Business Analysis & Requirements Leadership
Stakeholder Engagement & Discovery
Lead comprehensive requirements gathering using diverse methodologies (workshops, interviews, process mapping, surveys) Facilitate analytical discovery sessions with clinical and operational leaders Map complex healthcare workflows to identify analytics opportunities Build deep understanding of departmental value chains and pain points
Solution Design & Consulting
Translate business problems into analytical solution architectures Create business cases for predictive analytics initiatives Lead end-to-end analytical solutions spanning reporting to ML Present complex analytical concepts in business-friendly language Develop roadmaps aligning analytics capabilities with business strategy
Project Leadership
Lead cross-functional analytics projects from conception to value realization Manage stakeholder expectations throughout project lifecycle Ensure analytical solutions integrate seamlessly with business processes Measure and communicate business impact of deployed solutions
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