While interning at the city planning department, I was tasked with evaluating traffic accident data to recommend safety improvements for a high‑risk intersection.

My goal was to identify the primary causes of accidents and propose evidence‑based interventions within a 4‑week timeline.

I cleaned and merged three datasets (collision reports, traffic volume, and road geometry) using Python pandas, performed regression analysis to isolate significant predictors, and visualized findings in Tableau for stakeholders.

The analysis revealed that poor lighting and lack of turn lanes were the top factors. My recommendation to install LED signals and add a protected left‑turn lane was adopted, leading to a 12% reduction in accidents within six months.

At a nonprofit focused on affordable housing, a senior board member opposed our proposal to increase funding for rent‑control advocacy, fearing political backlash.

I needed to secure their endorsement to move forward with the funding request.

I prepared a briefing that combined recent research on rent‑control benefits, case studies from comparable cities, and a risk‑mitigation plan. I scheduled a one‑on‑one meeting, listened to concerns, and addressed each with data and a clear communication strategy.

The board member agreed to support the proposal, and the initiative secured $250,000 in funding, ultimately influencing local legislation that protected 3,000 households.

During a graduate research project, I was asked to evaluate a proposed regulation limiting industrial emissions in a mid‑size city.

My objective was to estimate the net economic impact over a 10‑year horizon.

I identified all relevant costs (compliance, monitoring, potential job losses) and benefits (healthcare savings, productivity gains, environmental improvements). I sourced data from EPA reports, local health statistics, and industry surveys, then built a discounted cash‑flow model in Excel applying a 3% discount rate.

The analysis showed a net benefit of $45 million over ten years, with health cost savings accounting for 60% of the total benefit, which helped the city council approve the regulation.

In my role as a junior analyst at a state agency, I needed to predict the impact of a new tuition‑free college program on enrollment rates.

Develop a reliable forecast to inform budget allocations for the next five years.

I employed a time‑series ARIMA model using historical enrollment data, supplemented with regression analysis incorporating variables such as median household income and high‑school graduation rates. I validated the model with out‑of‑sample testing and adjusted for policy lag effects.

The forecast projected a 15% increase in enrollment, which the budget office used to allocate an additional $12 million, ensuring program capacity met demand.

The council of a coastal city approached my consultancy to assess a proposed single‑use plastic ban aimed at reducing marine litter.

Provide a comprehensive evaluation framework covering environmental impact, economic effects, and equity considerations.

I designed a mixed‑methods approach: (1) baseline waste audit to quantify current plastic use; (2) life‑cycle assessment to estimate environmental benefits; (3) economic impact analysis using input‑output tables to gauge effects on local businesses; (4) stakeholder interviews (retailers, NGOs, low‑income residents) to identify equity concerns; (5) pilot program in two neighborhoods to collect real‑world data before full rollout.

The framework highlighted a 30% reduction in plastic waste and a modest 2% revenue dip for small retailers, mitigated by a proposed subsidy. Recommendations included a phased ban, public education campaign, and support for affected businesses, which the council adopted.

As part of a state economic development team, I was tasked with estimating how a proposed renewable‑energy tax credit would affect job creation over the next decade.

Develop an impact assessment model that isolates the credit’s effect on employment while controlling for broader economic trends.

I gathered data on historical renewable‑energy investments, employment figures by sector, and tax credit utilization rates from neighboring states. I built a computable general equilibrium (CGE) model to simulate how the credit would shift investment, using elasticity estimates from academic literature. I also incorporated multipliers from the Bureau of Labor Statistics to translate investment changes into direct, indirect, and induced jobs.

The model projected 4,500 net new jobs (2,200 direct, 1,500 indirect, 800 induced) over ten years, with the majority in construction and operations. The findings supported the policy’s inclusion in the state budget, and the credit was enacted with a monitoring framework.