Data and research analyst

Osman Zakaria, PMP

I find the errors, anomalies and cost leaks hidden in large records, then turn what I find into SQL checks, dashboards and models that others can rerun. I hold a Master of Professional Studies in Analytics from Northeastern University and bring 6+ years across payroll analysis, project coordination and research.

Osman Zakaria smiling, wearing a dark blazer over a blue shirt, standing in a lecture hall

Selected work

Seven projects, each with its method and limits written down. Where data is synthetic or comes from a survey, the project says so.

Public data

2,260 participatory processes. Which designs leave a mark?

Python, logistic regression, JavaScript

Participation Design Advisor

A tool for anyone planning a citizens' assembly, participatory budget or public consultation. You describe the design (who takes part, how much power they get, how decisions are made, whether it will be evaluated) and it estimates the chance the process documents real impact, ranks the changes that would raise it, and lists the closest past cases. It learns from 2,260 Participedia cases in 124 countries, plus 356 cases added to Participedia since. Planning a formal evaluation is the strongest lever (+11 points), yet only 15% of processes do it.

Method and limits

A regularized logistic regression (AUC 0.91 on held-out cases) scores each design in the browser. Every effect holds documentation fixed, because thin entries rarely record impact whatever happened. A context check shows how common a recruitment method is in countries like yours, from the companion research paper (under review). The repository also holds that paper's case classifier, a CatBoost and XGBoost ensemble that sorts the 2,260 cases into five method families (76% accuracy against baselines near 52%).

Documented impact is not proof of impact, and the archive is crowd-sourced. Associations, not causes. Not an official Participedia product.

Public data and simulation

86% of 524 real construction incidents flagged in advance

Python, gradient boosting, JavaScript

Site Safety Early Warning

An early-warning system for construction sites in British Columbia, Ontario and Alberta. Each work day it scores every zone's risk of an injury or high-potential near miss over the next five work days, names the warning signs, and cites the control from each province's OHS regulation. It also checks a three-week look-ahead for clashes (a crane lift in high wind, crowded zones), estimates the premium and COR savings from prevention, and takes near-miss reports in seven languages.

Method, real-world check and limits

The zone model is trained on 30 simulated sites calibrated to WorkSafeBC, AWCBC and Statistics Canada injury rates and run on real Environment Canada weather; it flags 84% of test-period events (AUC 0.75 against 0.66 for the crew schedule alone). Because real site logs are private, the approach was also tested on New York City's public records: across 37,000 sites, red-flagged weeks carried 11.6 times the average incident rate.

The site logs are simulated, so a pilot on a contractor's own records is the real test. Independent work, not affiliated with WorkSafeBC, the WSIB or Alberta OHS.

Public data

$83.7B signed. $98.7B after changes. Where does the extra $15B come from?

Python, regression, open data

Change Orders: federal contracts, signed at one price and finished at another

An interactive analysis of how much Government of Canada contracts grow after signing. It follows 351,872 federal contracts of $10,000 or more, awarded April 2017 to March 2023, through every amendment for three years. 13% were amended and 7.7% grew by 25% or more. The top 1% of contracts account for 81% of all dollars added after award.

Method and limits

The page shows what drives growth by procurement method, commodity, value, length and department, and includes a risk check that estimates the chance a new contract grows 25% or more from what is known at award, a watch list of recent high-risk awards, and the 40 largest increases. The risk model is a regularised logistic regression trained on awards to March 2021 and tested on the two years after. Reviewing the riskiest 10% of new contracts catches 39% of those that later grow 25% or more, which helps target change control where it matters.

An amendment is not an overrun: many large contracts grow by design through options and phases. Rates are a floor because some amendments cannot be matched to an award. Independent analysis of public data, not affiliated with the Government of Canada.

Public data

Two plants idle. 3,960 workers laid off. What’s next?

Python, JavaScript, road network

Next Shift: Ontario autoworker job match

An interactive tool for workers laid off at Stellantis Brampton and GM CAMI Ingersoll. A worker picks a plant job and a longest drive, and the tool ranks Ontario occupations by skill fit (using ESDC's OaSIS skills ratings), openings from Job Bank within a rush-hour drive on the Statistics Canada road network, pay, and training time, then lists nearby college programs and funding. It is built on 89,557 Job Bank postings, 516 occupations and Statistics Canada road-network drive times.

Planning views and limits

A planning view estimates how many of these workers local openings could absorb within a year and where retraining helps most. A new-products view tests how much of a retooled plant's hourly workforce the laid-off workers could fill.

Independent analysis from public data. Postings are not hires, and the plant workforce mix and the share of openings workers can win are adjustable assumptions, not union data.

Public and licensed data

$487M modelled annual saving

Python, Census 2021, optimization

Canada Post turnaround analytics

An interactive analysis of how Canada Post can cut delivery cost and win back parcel customers. The delivery model combines Canada Post's 2017 delivery-point and delivery-mode data with the 2021 Census and the National Address Register (10.7M buildings) across all 1,646 forward sortation areas. It estimates the saving from converting door-to-door delivery to community mailboxes, ranks the areas to convert first, and, in three case-study areas, sites mailboxes and plans routes on real street networks from actual depots.

Churn model, parcel targets and limits

It also includes an early-warning model for business customer churn, a ranked sales call list, and an allocation of the 79 million-piece (32.6%) drop in 2025 parcel volume across areas by modelled demand, to set win-back sales targets.

Independent analysis, not produced by or affiliated with Canada Post. The churn model runs on 8,000 simulated business customers because no real customer data is public. The saving is a model estimate based on stated assumptions; it is above the government's ~$400M figure, and route-level checks suggest it is conservative.

Public DOT data

96 of 96 DOT figures reproduced

Python, SQL, SQLite

U.S. flight delay pipeline, January to July 2026

An end-to-end Python and SQL pipeline that loads seven monthly Bureau of Transportation Statistics files (4,120,342 domestic flights, 13 carriers, 357 airports) into SQLite, runs 12 data-quality checks, and recomputes on-time rates for every carrier and month. All 96 recomputed figures match the Department of Transportation's published Air Travel Consumer Report within its rounding band.

Findings and limits

On-time rank and disruption risk differ. Southwest is 7th of 13 on on-time arrivals (73.7%) but has the second-lowest rate of 3-hour-plus delays, 78 per 10,000 flights against 320 for PSA. Delays also cascade through the day: 91.5% of flights scheduled at 5 a.m. arrive on time, against 66.3% at 8 p.m.

A Simpson's paradox check of all 78 carrier pairs found rankings that reverse at comparable airports. American trails Southwest overall (71.8% against 73.7%) but is 2.2 points ahead at the same airports in the same months, with a 95% interval of +1.9 to +2.4. American wins about half of the compared airport-months, so the lead comes from the large ones.

Covers reporting carriers only. Delay causes are the airlines' own attribution, and "late aircraft" delay can start with weather on an earlier leg.

Primary survey data

98% pay by mobile money. 22% trust AI to tax them fairly.

Python, survey statistics, dashboards

AI and big data for African tax reform: Africa's informal sector

The evidence base for a policy paper on widening the tax base in Sub-Saharan Africa's informal sector, with Ghana as the case study and Kenya and South Africa as comparators. A Python pipeline turns 53 hand-coded questionnaires from artisans, traders, drivers and farmers, a survey of 21 tax and finance professionals and MTN Ghana's responses into tested statistics, a check of the paper's figures and seven dashboards. The rails for digital taxation exist; trust does not: 74% cite distrust of government as a reason they don't pay tax.

Findings and limits

Awareness, not income, decides acceptance: 56% of workers who have heard of AI view it positively, against 3% of the rest (Fisher exact p < 0.001), while income shows no link (Spearman ρ = −0.07). Workers fall into three readiness segments (26% ready, 35% persuadable, 39% resistant), each needing a different policy. Professionals rank transparency in how taxes are spent as the strongest compliance lever. 27 of the paper's 29 figures match the coded forms within 5 points.

A small, purposive Ghana sample: it shows patterns and policy levers, not national prevalence.

More work

Canadian household energy, 2011 to 2021

Public data: Power BI, SQL, SQLite

A Power BI dashboard on Statistics Canada data for 10 provinces, with slicer filtering and DAX measures built around a 70.84 GJ national benchmark. A matching SQL analysis of 989 provincial observations ranks provinces against that benchmark (Alberta is highest at +11.4 GJ) and reproduces the dashboard figure, so the two tools cross-check each other.

Executive Portfolio Diagnostic Suite

Synthetic data: Tableau

A Tableau dashboard on a synthetic enterprise PMO dataset of 1M+ records across 50 projects. It tracks resource allocation, cost variance and schedule performance.

Where the skills come from

The professional work behind the projects.

Sept 2026 to present
AI Data Analysis Task Developer

Handshake AI, contract

Reviews AI-generated analyses against rubrics and writes expert-level analysis tasks.

Dec 2024 to present
Research Analyst

Participedia

Machine learning, Power BI reporting and data validation.

2017 to 2023
Educational Project Coordinator

Ghana Education Service

Trained 50+ staff on data tracking and coordinated multi-site schedules.

2014 to 2017
Payroll Project Coordinator

Controller and Accountant General's Department, Ghana

Reconciled payroll records for 600,000+ employees and flagged anomalies behind US$1.3M in annual savings, during a World Bank-sponsored move to electronic salary validation.

Credentials and tools

CredentialsProject Management Professional (PMP), 2025. Master of Professional Studies in Analytics, Northeastern University. BSc Business Administration, Kwame Nkrumah University of Science and Technology.
ToolsPower BI (DAX, Power Query), Tableau, SQL (PostgreSQL, MySQL, SQLite), Python (Pandas, Matplotlib, NumPy), advanced Excel.

Let’s talk about your data.

I am open to data analyst, business analyst, research analyst and similar roles in Vancouver and across Canada.