Senior Data Analyst at TD Bank (2025-07 – Present)
Environment: Python, SQL, Power BI, Tableau, Databricks, Snowflake, AWS Redshift
- Analyze large financial, customer, risk, and operational datasets using Python and SQL to identify patterns, anomalies, performance changes, and business-relevant insights.
- Perform detailed post-process analysis and data validation across source and downstream datasets, investigating unexpected results and documenting root causes, impacts, and recommended actions.
- Create concise analytical reports, visualizations, KPI views, and management-ready summaries that translate complex findings into actionable information for stakeholders.
- Collaborate with developers, data engineers, and business teams to investigate data issues, validate analytical logic, and enhance reusable reporting and analysis solutions.
- Use Python for data manipulation, exploratory analysis, automation, and repeatable validation workflows across large datasets.
- Apply statistical, forecasting, segmentation, and anomaly-analysis techniques to uncover trends and support evidence-based decisions.
- Track analytical issues through resolution, coordinate follow-ups with technical teams, and maintain clear documentation of findings, validation steps, and outcomes.
- Automate recurring analytical and reporting processes using Python and SQL, improving timeliness, consistency, and operational efficiency.
Data Analyst at Aviva (2022-04 – 2025-06)
Environment: Python, SQL, SAS, R, Tableau, QlikView, Informatica, Talend
- Analyzed large customer, policy, claims, financial, and operational datasets using Python, SQL, SAS, and R to identify patterns, exceptions, trends, and drivers of business performance.
- Conducted exploratory and statistical analysis to investigate unusual outcomes, customer behavior, claims patterns, and operational events and converted findings into business recommendations.
- Developed Tableau and QlikView reports and visualizations to communicate analytical findings clearly to business and technical stakeholders.
- Performed reconciliation, QA/QC, and data-quality analysis across multiple systems, investigating discrepancies and coordinating resolution with development and data teams.
- Collaborated with technical teams to validate transformation logic, improve analytical datasets, and troubleshoot issues affecting downstream analysis and reporting.
- Automated recurring data preparation, exception analysis, validation, and reporting using Python and PowerShell to reduce manual effort.
- Documented analytical methods, issue findings, data mappings, test outcomes, and follow-up actions to support reliable and repeatable analysis.
Data Analyst at GSK (2020-08 – 2021-12)
Environment: Python, SQL Server, SAS, R, Tableau, Excel, Talend, ETL
- Analyzed sales, operational, manufacturing, and supply-chain datasets to identify trends, anomalies, performance issues, and opportunities for improvement.
- Used Python, SQL, SAS, and R for data manipulation, statistical analysis, forecasting, and exploratory analysis across large multi-source datasets.
- Built Tableau reports and visualizations that summarized analytical findings and supported stakeholder decision-making.
- Investigated data and processing issues, performed root-cause analysis, validated ETL outputs, and worked with technical teams on corrective actions.
- Automated repeatable analytical and reporting tasks using Python and PowerShell and maintained supporting technical documentation.
Data Analyst at General Mills (2019-06 – 2020-07)
Environment: Python, SQL, Power BI, Looker, R, Scikit-learn, AWS Redshift, Snowflake, Apache Airflow
- Analyzed sales, demand, inventory, customer, and operational datasets using Python and SQL to uncover patterns, anomalies, and performance trends.
- Applied regression, forecasting, clustering, and exploratory analysis using Python, R, and Scikit-learn to support data-driven planning and decision-making.
- Developed Power BI and Looker reports and visualizations that converted analytical results into clear business insights.
- Collaborated with analytics and engineering teams to validate data pipelines, troubleshoot data issues, and improve reliability of analytical datasets.
- Automated recurring data preparation and analytical workflows using Python and Apache Airflow, improving repeatability and efficiency.
