Experience
Client-facing analytics experience across operations, research, and reporting.
These roles show how I work with stakeholders, define useful metrics, build reporting systems, and translate technical analysis into decisions people can act on.
I contribute to research on how large language models can support interview-based qualitative research without replacing human interpretation. The work combines prompt design, transcript generation, NLP evaluation, and qualitative research methods.
What I did
- Built a GPT-based simulation pipeline that generated AI interview transcripts for 28 founder personas using consistent structured prompts.
- Compared AI-generated and human interview responses across tone, depth, factual accuracy, strengths, weaknesses, and alignment gaps.
- Developed a Python NLP workflow for thematic coding and sentiment scoring so transcript analysis could be evaluated more systematically.
- Helped design a qualitative evaluation framework with faculty mentors that balances quantitative signals with human interpretation.
Client relevance: this supports my applied AI consulting offer because it shows I can build AI workflows with evaluation, structure, and responsible human oversight instead of vague automation.
Through NIU's Experiential Learning Center, I worked on a consulting engagement for a Chicago-based recording studio. The project required turning ambiguous growth goals, fragmented market information, and competitor activity into a structured recommendation set for leadership.
What I did
- Led business analysis across SEO performance, engagement metrics, campaign KPIs, competitor positioning, and customer segments.
- Analyzed 15+ competitor studios to define audience segments, market opportunities, and measurable success criteria.
- Translated fragmented market and demand data into KPI frameworks and strategic recommendations around growth, audience targeting, and marketing investment.
- Owned deliverables from data gathering through executive presentation, incorporating feedback so the final recommendations were practical for a real client.
Client relevance: this is direct consulting proof. It shows discovery, stakeholder communication, market analysis, structured recommendations, and presentation to non-technical leadership.
At Shipt, I worked on network planning analytics for a large delivery operation. The role gave me hands-on experience building analytics systems for operational teams that needed better visibility into ZIP-code coverage, daily order volume, and market-level performance.
What I did
- Designed and deployed a Tableau geospatial analytics platform analyzing daily order volume across 30,000+ ZIP codes.
- Helped Market Operations surface delivery blind spots, misconfigured ZIP-code coverage, and high-risk network regions with 30% better KPI visibility.
- Built SQL and Python ETL pipelines to process daily delivery data and automate Tableau extract refreshes, eliminating 10+ hours of manual reporting per week.
- Partnered with Market Operations and Network Planning stakeholders to translate ambiguous operational questions into dashboard requirements and reliable reporting outputs for 5+ planning teams.
Client relevance: this is the foundation for my operations intelligence offer. It proves I can build dashboards, automate reporting, define KPIs, and connect analysis to real operational decisions.
At Innovation DuPage, I supported reporting and operational analysis across program areas. The work focused on making program performance easier to measure, improving Salesforce reporting visibility, and giving leadership more consistent KPI definitions.
What I did
- Built and maintained Tableau dashboards tracking 20+ KPIs across program areas.
- Standardized metric definitions and reporting logic, reducing reporting cycle time by 40%.
- Used SQL and Excel transformation workflows to clean and structure raw program data for downstream analysis.
- Applied Excel-based modeling to identify engagement, utilization, and cohort trends that informed outreach strategy and resource allocation.
- Improved Salesforce CRM reporting workflows so stakeholders had better visibility into program performance.
Client relevance: this maps directly to small business, startup, nonprofit, and incubator-style BI consulting where teams need cleaner KPIs, dashboards, and reporting routines.
My leadership and competition experience shows that I can communicate technical work publicly, organize professional development opportunities, and present analytics recommendations to people outside the classroom.
What I did
- Won 1st Place in the NIU Data Visualization Competition with HAVI by building a Power BI dashboard using Python and Excel to analyze QSR supply chain inefficiencies.
- Presented findings and recommendations to an industry panel of supply chain professionals and data scientists.
- Founded and chaired OMIS IGNITE, a student-led initiative connecting Operations and Information Management students with professional development, speaker panels, workshops, and networking opportunities.
- Supported NIU's computing community through ACM by promoting events, encouraging student involvement, and helping peers connect with technical and professional opportunities.
Client relevance: consulting depends on trust and communication. This section shows that I can lead, present, organize, and explain analytics work in front of real audiences.