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 effortsDesign, develop / code, and validate machine learning modelsConduct advanced statistical analysis to derive model selection and trainingModel Development : Lead end-to-end ML project development including EDA, feature engineering, model selection, training, and validationAzure ML Implementation : Oversee design and implementation of ML pipelines using Azure ML, including model deployment, monitoring, and retrainingStatistical Analysis : Conduct advanced statistical analysis, hypothesis testing, and model validation using appropriate methodologiesTechnical Team Leadership
Project Management : Lead cross-functional ML projects from conception through deployment and monitoringPeer Review : Conduct technical reviews of ML models, code quality, and deployment strategiesLead and mentor data scientists and analystsEstablish technical standards for ML developmentOversee and Collaborate with data engineers on ML pipeline designIdentify and help prioritize machine learning use cases across the organizationChampion adoption of predictive analytics in operations – this includes presenting results, solutions and their applicationGoTool Platform Involvement
Manage and evolve the GoTool AI / MLOps platform (our in-house AI / ML platform)Ensure platform reliability and performanceDrive platform enhancements based on user needsManage quarterly model refreshes and updatesCoordinate with stakeholders on platform roadmap2. 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 leadersMap complex healthcare workflows to identify analytics opportunitiesBuild deep understanding of departmental value chains and pain pointsSolution Design & Consulting
Translate business problems into analytical solution architecturesCreate business cases for predictive analytics initiativesLead end-to-end analytical solutions spanning reporting to MLPresent complex analytical concepts in business-friendly languageDevelop roadmaps aligning analytics capabilities with business strategyProject Leadership
Lead cross-functional analytics projects from conception to value realizationManage stakeholder expectations throughout project lifecycleEnsure analytical solutions integrate seamlessly with business processesMeasure and communicate business impact of deployed solutions