SELECTED WORK

Problems solved.
Software shipped.

A sample of tools built for real organizations — each one designed to eliminate a manual process, surface better information, or give a team back hours they shouldn't have been spending in the first place.

01 / PROJECT
Proximity Dispatch Tool
CLIENT: NURSE STAFFING & HOME CARE COMPANY
React Node.js Mapping API Microsoft SSO Home Care Scheduling

Smart, location-aware scheduling for home care and nursing staffing. Instead of manually cross-referencing who is available and who is nearby, schedulers select an open shift, a client, or a caregiver and instantly see the best matches ranked by travel time, complete with availability, weekly hours, and preferred or exclusion status. What used to take phone calls and spreadsheets now takes seconds.

WHAT IT DOES
Proximity matching, both directions. Pick a shift or client to find the closest available caregivers, or pick a caregiver to surface the nearest open shifts and client locations.
Real travel times. Driving distance and estimated public-transit time for every match, plus one-click directions.
Overtime protection. Each caregiver's weekly hours are shown at a glance, with clear alerts before anyone crosses 40 hours.
Applicant proximity. For recruiting, see which clients are nearest to a new applicant to guide placement conversations.
One-tap outreach. Call caregivers directly through Microsoft Teams from any result.
IMPACT
Turns a slow, manual matching process into an instant, data-driven one, helping agencies fill shifts faster, reduce travel burden on caregivers, avoid accidental overtime, and give every client a well-matched nearby care partner.
React SPA Node.js backend Microsoft SSO Routing engine Cloud hosted CI/CD deployments
02 / PROJECT
Teacher-Student Oracle
CLIENT: CAREER COACH
AI Assistant Anthropic Cloudflare Supabase Slack integration RAG

A production AI coaching assistant for a career transition program, available to students around the clock. It answers questions grounded in course material, the Slack community, and session notes, speaks in the program's voice, and keeps its knowledge current automatically. Students can sign in so it remembers their progress across sessions.

WHAT IT DOES
Always-on student support. A web-based chat interface students can use any time, with optional sign-in that saves progress and personalizes guidance as conversation history accumulates.
Automatically current knowledge base. Syncs the Slack community weekly, including private channels, alongside course material and session notes, so the Oracle always reflects the latest content.
Source-weighted answers. Instructor voice in Slack is trusted most, followed by session notes and course material. Student peer discussion informs but does not override the teaching.
Admin dashboard. The coach sees exactly which students use the Oracle, how much, and when, with a content uploader for adding new material at any time.
Smart nudges. After 45 active minutes, the Oracle surfaces a prompt to schedule a call with the coach, bridging AI support with human coaching at the right moment.
IMPACT
Seven of nine planned improvements are live and in daily use. Automated content updates mean the coach never has to manually refresh the knowledge base. Students get consistent, on-brand guidance at any hour without adding to the coach's workload.
Anthropic API OpenAI embeddings Cloudflare Workers Supabase Resend email Slack API
03 / PROJECT
Trade Intelligence Platform
CLIENT: MAJOR U.S. MARITIME PORT
Snowflake Power BI Azure Blob SQL ERD design Trade data Executive reporting

A modern trade intelligence platform built for one of the most active cargo ports on the U.S. East Coast. The port handles over 36 million tons of cargo annually, reached a record 841,000 TEUs in 2024 (up 13% year over year), and is ranked #1 in North America for container productivity by the World Bank and S&P. This project replaced a fragmented, manual reporting environment with a unified data warehouse, dimensional model, and executive Power BI dashboards that update automatically as new data arrives.

WHAT IT DOES
Entity relationship model designed for scale. A dimensional data architecture built in Snowflake supports a core shipments fact table with 110+ million records, joined to dimension tables for port details, commodity classifications, trade representatives, and shipping lines.
Two ingestion paths. A simple bulk upload path handles files up to 250MB directly in Snowflake. An Azure Blob Storage external stage handles larger files and batch loads, with automated COPY INTO commands staging data without size constraints.
Snowflake views as the reporting layer. Aggregated views join the shipments table to dimension tables, coerce data types, and feed Power BI reports via Direct Query, so dashboards always reflect the latest certified data without manual refreshes.
Multiple report environments. Separate warehouses for data loading and reporting eliminate performance conflicts. A shared Power BI workspace gives approved stakeholders access to East Coast, local, and US trade standard reports without each requiring individual file distribution.
Flexible regional scoping. Dimension tables allow analysts to include or exclude ports from metro or regional groupings at reporting time, without touching the underlying data. Cargo assigned to representatives updates downstream automatically.
IMPACT
A port tracking over $7.3 billion in annual food cargo and nearly 282,000 vehicle imports in 2024 now has a reporting foundation that scales with its 15-year growth plan. Analysts can run live queries across 110+ million shipment records without disrupting end-user reporting, and trade representatives see current cargo assignments the moment new certified data is loaded.
Snowflake Azure Blob Storage Power BI Direct Query SQL views PIERS trade data ERD modeling
04 / PROJECT
Field Operations Platform
CLIENT: PRESSURE WASHING CONTRACTOR
Mobile app Time tracking Quoting Photo docs Field ops South Jersey

A purpose-built field operations tool for a fully insured, licensed pressure washing and exterior cleaning contractor serving residential, commercial, and industrial clients. Replaces manual timesheets, handwritten quotes, and unorganized job photos with a single mobile-first system crews can use on-site.

WHAT IT DOES
Clock in and out from the field. Employees record start and end times from any device, eliminating paper timesheets and ensuring accurate payroll data without a back-office reconciliation step.
Quote finalization on-site. Crews can review and lock in job details and pricing before leaving a property, reducing follow-up calls and keeping estimates tied to actual conditions.
Before and after photo documentation. Structured photo capture at job start and completion creates a timestamped record for every job, supporting quality assurance, dispute resolution, and client communication.
IMPACT
Gives a lean field crew the documentation and operational discipline of a much larger company, with zero extra back-office burden. Every job leaves a complete, timestamped record: who was there, what was agreed, and what the work looked like before and after.
Mobile-first Time tracking Photo capture Quote management Cloud storage
05 / PROJECT
Statistical Expert Report
CLIENT: CONSTITUTIONAL LAW FIRM
Applied statistics Expert testimony Survey methodology Sample size analysis Chi-squared testing Litigation support

A signed statistical expert report prepared for litigation support, analyzing whether a defendant's collection of 77 affidavits constituted a valid survey capable of being generalized to the broader population of host representatives. The report identified fundamental flaws in experimental design and statistical methodology, quantified the gap between the submitted sample and a properly constructed one, and proposed the correct survey design parameters had a rigorous approach been taken.

KEY FINDINGS
Selection bias invalidated the sample. Affiants were not selected through random sampling, introducing significant selection bias and undermining the ability to generalize findings to the population of approximately 588 host representatives who held 3,764 events during the relevant period.
Effective sample was far smaller than claimed. Nearly 30% of affidavits cited events outside the relevant date range, and another 13% did not cite event dates at all. The usable affiant count was reduced from 77 to 44 after applying date-range exclusions.
Respondents were not independent actors. Many affiants had affiliations with the defendant or with one another, violating basic principles of unbiased response collection and compounding the selection bias problem.
Survey instrument lacked methodological rigor. The three affidavit forms lacked the structure, neutrality, non-response inclusion, and anonymization required of a valid survey. Language relied on stated belief rather than demonstrated understanding and did not control for confounding variables.
Required sample size calculated and documented. Using a Chi-squared proportional means test framework with 95% confidence, 80% power, and a finite population correction for the 588-person population, the minimum valid sample was calculated at 468 respondents under equal proportions — ten times the effective affiant group.
DELIVERABLE
An 18-page signed expert report submitted under professional certification, covering population definition, distribution analysis, date-range exclusions, sample independence, instrument quality assessment, Chi-squared test design, finite population correction methodology, and a scenario for calculating an appropriate sample size. Conclusions held to a reasonable degree of professional certainty.
Applied statistics Chi-squared testing FPC methodology Survey design Sample size calculation Expert testimony
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