2-3 sentences or 2-3 bullet points that include your years of professional experience, accomplishments, top skills and strengths as they relate to the position and what you’re looking for in my teaching role for the following resume (while focusing on teaching experiences):
•	Technical Skills: Python (along with Python packages: Pandas, Sci-kit learn, Matplotlib, etc.), SQL, Hadoop, Tableau, Power BI, Salesforce, Git, GitHub, SharePoint, Jira, Confluence, MS Office, Excel, MATLAB, R.

•	Certificates: PSM I (Professional Scrum Master I), IBM Data Science Professional Certificate; IBM Python for Data Science, AI and Development; IBM Machine Learning with Python; IBM Databases and SQL for Data Science; IBM Data Analysis with Python; IBM Data Visualization with Python.
  Work Experience
 
  Senior Business Analyst, Penfed Credit Union	 September 2021 – Present  
•	Founding member of Data Analytics and Reporting team at Penfed Credit Union. 
•	Top 10% performer at Penfed Credit Union, recognized as ‘Exceptional in All Areas’.
•	Implemented and documented system and business process changes to support consumer banking reporting and reduced time execution for downstream reports by 94% overall for high-impact reports and modeling projects.
•	Enhanced data quality and reduce variances from 20-50% to < 5% for downstream compliance reporting.
•	Automated existing and new reporting using SQL and Python, reducing data sources used by 85%.
•	Standardized data definitions for some data products across teams for organization-wide internal reporting.
•	Subject matter expert on data warehouse and worked on projects for more than 10 departments (Product teams, Finance, Accounting, Operations, etc.) providing reporting and insight on consumer banking products and best practices with respect to their business needs.
•	Presented regularly and directly to executive leadership (C-Suite executives).

  Data Analyst, UVA School of Medicine Anesthesiology Department	December 2018 - May 2020
•	Conducted research on child epilepsy using patient-driven mutations in mouse models.
•	Collected data for computational work using Neuron, a python environment that uses the different parameters of neurons to create neuron models, to explore and identify new causes of epilepsy.
•	Compiled over 10,000 data points per data set for a total 26 data sets using Excel.
•	Presented findings on neuron excitability and its effects on epilepsy to the Principal Investigator and faculty advisors as well as published in the Journal of Neuroscience as a contributing author.

  Teaching Assistant, UVA  Anesthesiology Department	December 2018 - May 2020
•	Conducted research on child epilepsy using patient-driven mutations in mouse models.
•	Collected data for computational work using Neuron, a python environment that uses the different parameters of neurons to create neuron models, to explore and identify new causes of epilepsy.
•	Compiled over 10,000 data points per data set for a total 26 data sets using Excel.
•	Presented findings on neuron excitability and its effects on epilepsy to the Principal Investigator and faculty advisors as well as published in the Journal of Neuroscience as a contributing author.


Projects
 
Data Analyst, UVA Forge Node Course, Charlottesville, VA	January 2020 - May 2020
•	Led a team to determine frequency of crimes using the Charlottesville Open Database and classified results by streets using a regression model.
•	Cleaned data using Python Pandas package to extract insights and aid in decision-making; graphed using Matplotlib.
•	Designed a Canva infographic to convey technical knowledge and recommendations to a non-technical audience; presented details of analysis to a class of 25 peers and CEO, who transferred recommendations to the university for further use.

Data Analyst, UVA Career Accelerator + Radify Labs	December 2019 - January 2020
•	Completed 5 projects to learn and refine skills in data analysis and visualization using Python, Tableau.
•	Collaborated with team members to execute projects focused machine learning models.
•	Analyzed large data sets regarding food accessibility and socioeconomic vectors from 2010 to 2014 leveraging K-means to predict accessibility outcomes in 2015 using Python.