Ai Research Analyst

Description:

Reporting to Heather Howley, and with co supervision by Drs. Kevin Dick (AI Data Scientist, BORN Ontario) and Christine Armour (Medical Director, Prenatal Screening Ontario and Investigator, CHEO Research Institute), the AI Research Analyst will:

• Prepare study datasets for analysis, including integrating data from various sources, data filtering, and data cleaning.

• Organize and maintain datasets to ensure data accuracy, quality, integrity, and protection of personal health information.

• Conduct initial data explorations and analyses, using statistical tools and models to identify trends, patterns, and relationships within the data.

• Work with the team to identify new data sources and provide input into data transformation rules required to prepare data for advanced analytics use cases.

• Contribute to the design, implementation, and iterative improvement of an appropriate data science and machine learning framework to support the project goals.

• Contribute to the development and deployment of appropriate data science and ML models to derive outcomes and obtain value from the data.

• Train, evaluate, and retrain predictive models and AI/ML systems as needed.

• Perform statistical analysis to report on experimental results and model testing.

• Prepare output of analyses for review including graphs, tables, figures, reports, presentations, manuscripts, and interpretation of experimental results.

• Maintain documentation, code, analysis, and results so work can be understood and repeated in accordance with coding best practices.

• Contribute to insightful presentations, communicating technical material, and growing others’ technical understanding.

• Support innovation by proactively identifying and championing projects that solve complex problems through the application of data science and ML techniques.

QUALIFICATIONS, SKILLS, AND ABILITIES

• Master’s degree in applied epidemiology/biostatistics /computer science/data science, or equivalent, with a concentration in data science and machine learning programming and techniques. (Required)

• Supporting degree or education in biology/biochemistry, biostatistics, bioinformatics, computational biology, computer science, applied mathematics, machine learning, perinatal research or related scientific fields with 2+ years’ working experience in computational biology, bioinformatics and/or machine learning in pharma, biotech or academic setting. (Preferred)

• Solid understanding of data structures, data modeling and software architecture. (Required)

• Mastery of one or more analytical programming languages including as R and Python
  and the ability to learn and adapt to new programming languages as needed. (Required)

• Experience/mastery of related scientific programming languages including SAS, Stata, considered an asset. (Preferred)

• Demonstrated experienced with specialized and standardized data science techniques (e.g., regression, segmentation, deep learning, feature importance, model explainability, experiment logging, model drift monitoring, etc.). (Required)

• Demonstrated experience with data visualization techniques and tools for effective presentation of results. (Required)

• Ability to define and develop data analysis strategies under minimal supervision with high degree of scientific competence and organizational attention to detail. (Preferred)

• Knowledge of quantitative techniques (normalization, standardization, applied statistics) for clinical results. (Preferred)

• Experience working with large-scale healthcare data sets. (Preferred)

• Experience with Transformer-based ML architectures. (Preferred)

• Experience working in high-performance computing environments. (Preferred)

• Outstanding analytical and problem-solving skills. (Required)

• Concise non-expert communication of scientific concepts, analyses, and reporting. (Preferred)

• Excellent communication skills, verbal and written. (Preferred)