Research Data Analyst supporting data analytics and research activities at Scarborough Health Network. Collaborating with investigators and utilizing statistical expertise for research projects.
About the role
- Senior Data Analyst utilizing BigQuery and SQL Server to analyze transportation data for Ford. Leading investigations and collaborating across teams to support data-driven decision making.
Responsibilities
- Lead timely analysis for evolving, high-priority stakeholder questions; clarify problem statements, align on definitions, and deliver accurate findings with supporting evidence
- Analyze time-ordered event/telemetry data at scale to establish context around key outcomes, identify precursor signals, and measure the frequency of specific event sequences
- Write efficient, well-structured SQL for analytical workflows, including heavy use of window functions for sequencing, sessionization, and 'leading up to' / 'following' event context
- Analyze non-time-series operational datasets in SQL Server; join across tables, validate definitions, and reconcile findings with event-based analytics where applicable
- Assess data quality and result validity, identify anomalies, form and test hypotheses, and iterate analyses as new information emerges
- Integrate and reason across multiple data sources to provide a coherent view of what happened and under what conditions
- Work with stakeholders to define detection requirements, author and refine rule logic for our log-pattern analysis tool, execute investigations, and summarize findings along with recommended rule improvements
- Deliver outputs ranging from concise executive summaries to detailed technical write-ups that document methods, assumptions, and contributing factors
- Work closely with engineering, product, and process-improvement stakeholders (including Six Sigma/Black Belt practitioners) to frame questions, define metrics, and support structured root-cause analysis efforts
- Communicate complex findings calmly and clearly in high-visibility settings; distinguish evidence from hypotheses and avoid premature conclusions
Requirements
- Bachelor’s degree in Data Science, Computer Science, Statistics, Engineering, or a related quantitative field
- Minimum 3 years experience in data analysis, including time-series/event correlation and relational (non-time-series) analysis
- SQL, including expert use of analytical/window functions with BigQuery and SQL Server (or equivalent relational database) experience
- Writing performance-conscious queries over large datasets (e.g., partitioning/clustering-aware approaches, minimizing unnecessary scans, and validating results efficiently)
- Minimum 5 years of experience in visualization/reporting tools such as Power BI or Looker Studio
- Familiarity with structured problem-solving/continuous improvement approaches (e.g., Six Sigma, DMAIC) and comfort collaborating with Black Belt–level stakeholders
Benefits
- successful candidates will be required to provide proof of degree completion for the highest level of education attained
- if the degree was obtained from a school outside of Canada, an Education Credential Assessment report showing Canadian equivalency is also required
- identification of anomalies, validation of assumptions, and use of data to test hypotheses and refine problem statements
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