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:
1. Data Science & Machine Learning
2. Python programming (NumPy, Pandas, Scikit-learn, TensorFlow/PyTorch)
3. Experience leading discovery shops is mandatory (not just getting requirements from BAs)
4. Strong statistical knowledge and experimental design
5. Experience with Azure ML or similar cloud ML platforms is a strong asset
6. Model lifecycle management experience
7. Python, Pandas, GeoPandas
8. Analytics & BI Understanding
9. General knowledge of modern BI/analytics platforms
10. Experience with SQL and data manipulation
11. Proven track record and extensive experience leading requirements gathering for complex analytical projects
12. Proven track record of translating business needs to technical solutions
13. Experience with process mapping and workflow analysis
Key Responsibilities:
1. Data Science & Machine Learning Leadership
Technical Development
14. Guide and mentor exploratory data analysis (EDA) and feature engineering efforts
15. Design, develop/code, and validate machine learning models
16. Conduct advanced statistical analysis to derive model selection and training
17. Model Development: Lead end-to-end ML project development including EDA, feature engineering, model selection, training, and validation
18. Azure ML Implementation: Oversee design and implementation of ML pipelines using Azure ML, including model deployment, monitoring, and retraining
19. Statistical Analysis: Conduct advanced statistical analysis, hypothesis testing, and model validation using appropriate methodologies
Technical Team Leadership
20. Project Management: Lead cross-functional ML projects from conception through deployment and monitoring
21. Peer Review: Conduct technical reviews of ML models, code quality, and deployment strategies
22. Lead and mentor data scientists and analysts
23. Establish technical standards for ML development
24. Oversee and Collaborate with data engineers on ML pipeline design
25. Identify and help prioritize machine learning use cases across the organization
26. Champion adoption of predictive analytics in operations – this includes presenting results, solutions and their application
GoTool Platform Involvement
27. Manage and evolve the GoTool AI/MLOps platform (our in-house AI/ML platform)
28. Ensure platform reliability and performance
29. Drive platform enhancements based on user needs
30. Manage quarterly model refreshes and updates
31. Coordinate with stakeholders on platform roadmap
2. Business Analysis & Requirements Leadership
Stakeholder Engagement & Discovery
32. Lead comprehensive requirements gathering using diverse methodologies (workshops, interviews, process mapping, surveys)
33. Facilitate analytical discovery sessions with clinical and operational leaders
34. Map complex healthcare workflows to identify analytics opportunities
35. Build deep understanding of departmental value chains and pain points
Solution Design & Consulting
36. Translate business problems into analytical solution architectures
37. Create business cases for predictive analytics initiatives
38. Lead end-to-end analytical solutions spanning reporting to ML
39. Present complex analytical concepts in business-friendly language
40. Develop roadmaps aligning analytics capabilities with business strategy
Project Leadership
41. Lead cross-functional analytics projects from conception to value realization
42. Manage stakeholder expectations throughout project lifecycle
43. Ensure analytical solutions integrate seamlessly with business processes
44. Measure and communicate business impact of deployed solutions