Accenture
I began working for Accenture in 2017, supporting multiple federal agencies through analytics, engineering, and product-oriented initiatives. My work spanned building data visualization and reporting platforms, developing predictive and machine-learning models, constructing ETL pipelines and dashboards, and collaborating with cross-functional teams to translate technical requirements into scalable applications and workflows.
Highlights
A few mini-projects / accounts I worked on during my time at Accenture:
DHS
Data Analyst
Built models + dashboards to improve staffing visibility and decision-making.
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USCIS
Senior Data Analyst
Automated recurring reporting and added validation to reduce manual effort.
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IRS
Analytics Consultant
Created views and narratives that helped leadership align on priorities.
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DHS
- Context: Accenture was brought on to help DHS increase agent hiring over a 5-year period.
- Responsibilities: Modeled the end-to-end hiring pipeline for 5,000 federal agents over five years. Owned and managed a $13M contract forecast incorporating financial and operational inputs. Built workforce and financial planning models using Microsoft Excel and SQL to analyze hiring funnel performance, including cost per hiring step, attrition and pass/fail rates, time-to-hire distributions, and seasonality. Developed key performance metrics and communicated model findings and recommendations to leadership and operations teams to align strategy with margin targets.
- Impact: Maintained ~15% operational income (OI) margin targets through proactive scenario planning. Improved visibility into hiring pipeline efficiency and enabled more informed budgeting and workforce decisions through structured forecasting and KPI reporting.
USCIS
- Context: Accenture was brought on to revamp the U.S. Citizenship and Immigration Services website by enhancing applicant web forms and leveraging data-driven tools to better serve users.
- Responsibilities: Developed and deployed predictive models to estimate processing timelines for ~8 million annual applicants. Used Python as part of the Big Data team to support large-scale forecasting and automation. Led exploratory data analysis using Jupyter Notebooks to clean, transform, and visualize datasets to identify trends and bottlenecks. Built scalable ETL pipelines with Databricks to process and load applicant data into Postgres, leveraging Amazon S3 and EC2 for large-scale data storage and processing. Coordinated with front-end teams to integrate APIs and a gradient-boosted FAQ classification model into live web forms.
- Impact: Improved accuracy of applicant processing time estimates across millions of users. Reduced applicant uncertainty through real-time wait-time visibility and automated FAQ routing while improving cross-team data accessibility.
IRS
- Context: Supported the IRS as part of an analytics R&D team delivering cybersecurity and workforce insights through data visualization and internal applications.
- Responsibilities: Led design and deployment of a vulnerability analytics application analyzing ~20,000 web vulnerabilities weekly. Built the interface using React and D3 while managing two junior developers. Spearheaded internal application deployments by collaborating with Linux infrastructure teams and translating requirements into automated Ansible jobs. Developed workforce and reporting dashboards using Tableau while conducting requirements gathering, wireframing, and UAT sessions with stakeholders.
- Impact: Increased hiring throughput by ~50% and reduced candidate time-to-hire by ~30 days through data-driven dashboards. Improved internal security visibility and reduced manual reporting effort through automated deployment and analytics tooling.