•Designed and Implemented Data Pipelines: Developed end-to-end data pipelines integrating Alteryx for data preparation, SQL for querying, Python for advanced data processing, and Power BI for visualization, ensuring efficient and accurate data flow.•Automated Data Processes: Utilized Alteryx to automate repetitive data extraction, transformation, and loading (ETL) processes, reducing manual effort and minimizing errors.•Optimized Data Workflows: Employed SQL to design complex queries and manage data storage, while Python scripts were used for data manipulation and cleaning, optimizing workflows and improving data quality.•Built Interactive Dashboards: Leveraged Power BI to create dynamic and interactive dashboards and reports, enabling stakeholders to gain real-time insights and make data-driven decisions.•Ensured Data Integrity and Accuracy: Implemented rigorous data validation checks across the pipeline to maintain data accuracy and integrity, leveraging Alteryx's built-in tools and custom Python functions.•Integrated Multiple Data Sources: Combined data from various sources using Alteryx, SQL, and Python, consolidating information into a unified dataset for comprehensive analysis and reporting.•Troubleshot and Refined Pipelines: Identified and resolved issues in data pipelines, employing Python for debugging and Alteryx for process refinement, ensuring smooth and reliable data operations.•Collaborated with Cross-Functional Teams: Worked with business analysts and other stakeholders to gather requirements, develop data solutions, and translate complex data findings into actionable insights using Power BI.

•Loaded client data to database tables and produce import summary reports. •Utilized basic to intermediate knowledge in SQL Server databases and SQL scripting to update databases and created tables, views, and procedures. •Assisted as required with client, internal, and workflow reports. •Retrieved and reviewed raw files from various file transfer methods (FTP, Email, SharePoint). •Scrubbed raw files for pertinent information, and transformed and imported data to database tables. •Produced complex SQL queries to collect important information from databases•Worked with clients and operations teams to meet expectations and produce poignant reports

• Assist the Retail Store Manager in planning and implementing strategies to attract customers• Coordinate daily customer service operations (e.g. sales processes, orders, and payments)• Track the progress of weekly, monthly, quarterly, and annual objectives• Evaluate employee performance and identify hiring and training needs• Supervise and motivate staff to perform their best• Monitor retail operating costs, budgets, and resources• Communicate with clients and evaluate their needs• Analyze consumer behavior and adjust product positioning

• Applied supervised and unsupervised machine learning algorithms to research questions• Generated creative and stunning visualizations of experimental data using R and SQL• Designed and performed hypothesis-driven experiments• Presented research findings at several international conferences• Analyzed large unstructured datasets to identify nuanced trends

• Successfully communicated research findings to committees of experts and non-experts alike • Prepared presentations on biological and statistical concepts to meet learning guidelines• Lectured for up to 50 students at once on various topics including statistics biology and technology• Demonstrated mastery of multiple complex topics to students with little previous experience

• Created informative visualizations of high-complexity data • Reviewed scientific documents for accuracy, formatting, and consistency, and compliance with protocols • Directed up to 10 undergraduates working in the lab on complex projects • Successfully managed and balanced multiple complex projects simultaneously

• Performed literature reviews and presented results of the reviews at weekly meetings• Designed plans of action and proposals based on literature reviews• Streamlined data analysis pipelines, improving both personal and lab productivity by 50%• Cleaned and organized unstructured datasets for analysis