Real Work.
Real Results.

The Problem

My client's HR team was manually creating and distributing individual payslip PDFs for over 200 members of staff every single month. This process required opening each employee's record, populating a template, saving as PDF, and distributing - repeated 200+ times. The entire process took days of manual effort every month, was prone to errors, and consumed valuable HR time that could be spent on strategic work.

The Solution

• Designed a master payroll template in Excel with a structured data input sheet for all 200+ employees
• Built a VBA macro that loops through every employee record automatically
• Macro populates each employee's individual payslip template with their specific data
• Automatically saves each completed payslip as a named PDF in a designated folder
• Process is repeatable every month with one button click - no technical knowledge required from the HR team

The Result

What previously took the HR team 2 to 3 days of manual effort every single month now takes under 3 minutes. The template is reusable indefinitely and scales as the company grows. Zero technical knowledge required to run it.

Tools Used

Overview

In collaboration with a skilled team of data analysts and a medical doctor, I led the development of a comprehensive Power BI solution to address critical inefficiencies in the healthcare system. The project delivered 7 integrated dashboards that give hospital administrators real-time insights across all major operational areas—from patient flow to financial health.

Challenge: The hospital lacked a unified view of operations, forcing leaders to make decisions based on outdated, siloed reports. This resulted in resource misallocation, bottlenecks in patient care, and missed revenue opportunities.

Solution: We built a data pipeline using Google Sheets as the data source, Azure DevOps for collaboration, and Power BI for visualization. The suite of 7 dashboards provides a holistic, real-time view of the hospital's performance.

Dashboard Breakdown

🏠 Homepage

High-level snapshot of critical operations: staff efficiency, patient satisfaction, inventory status, and financial health - all in one place for instant executive oversight.

📈 KPI Dashboard

Tracks total revenue, patient visit trends, appointment counts, staff/patient numbers, department distribution, and treatment outcomes across age groups and gender.

👩‍⚕️ Staff Dashboard

Detailed staff view: name, gender, department, certification, shifts, patients treated, and average consultation time. Helps optimize workforce allocation.

🧑‍🤝‍🧑 Patient Dashboard

Comprehensive patient view: demographics, health conditions, appointment schedules, and medication tracking to enable personalized care delivery.

📦 Inventory Dashboard

Tracks total stock items, restock levels, equipment utilization rates, maintenance schedules, and condition indicators (good/needs repair) by department.

📅 Appointment Dashboard

Interactive calendar showing appointment days with linked patient and staff details, enabling efficient scheduling and reduced wait times.

💰 Financial Dashboard

Overall revenue and expense tracking with department-level breakdown, identifying profit-generating units and areas of loss to drive financial strategy.

Key Outcomes

• Real-time decision-making: Hospital leaders can now monitor performance at a glance and take immediate action, rather than waiting for monthly reports.
• Resource optimization: Better staff allocation and inventory management reduced waste and improved patient care.
• Financial clarity: Department-level profit/loss visibility enabled strategic budget reallocation.
• Scalable framework: The dashboard structure is designed to adapt to other hospitals and healthcare settings across different countries.

Tools & Technologies

Background

A fast-growing Fashion and Beauty start-up specializing in makeup products, skincare, and haircare. As the brand scaled its distribution across multiple cities in the United States, its operations became increasingly complex - relying on diverse suppliers, varied transportation modes, and fluctuating demand. The company collected internal supply chain data to identify inefficiencies in inventory, shipping, and production processes.

The Problem

Despite high product demand, the company faced critical operational challenges:

• Frequent stockouts - 46% of products at risk of running out
• Rising shipping and manufacturing costs eating into margins
• Customer dissatisfaction due to delivery delays and defective items
• No clear visibility into supplier performance, route efficiency, or product profitability

Leadership needed to identify bottlenecks from supplier performance to route choices, so they could improve operational efficiency and reduce costs.

Key Business Questions Answered

• Stockout Risk: Which SKUs are at risk based on low availability and high order quantities?
• Supplier Performance: Which suppliers or routes are causing the most delays or high costs?
• Quality Control: Are high defect rates linked to specific suppliers or manufacturing lead times?
• Product Profitability: What is the net value per product considering revenue, shipping, and manufacturing costs?
• Transportation Impact: Is there a correlation between transportation mode (Air/Road/Rail) and shipping cost?

Key Findings & Recommendations

Tools Used

Overview

This dbt project transforms raw event data from the EasyFairs platform (demo dataset) into structured, analytics-ready datasets hosted in Google BigQuery. The goal was to build a reliable and scalable data pipeline that enables comprehensive reporting and key performance metric analysis for stakeholders.

The Problem

The EasyFairs team lacked a unified view of event performance across attendees, exhibitors, and sales. Data was fragmented across multiple sources, making it difficult to answer critical business questions:

• How successful are our events in terms of attendee satisfaction and sales?
• Which regions or event types generate the most profit?
• Are there correlations between exhibitor feedback and attendee satisfaction?

This project solves this by cleaning, unifying, and modeling raw data into actionable insights using dbt.

Data Pipeline Architecture

The project follows a layered architecture using dbt's modular approach:

• Raw Layer (Sources): External datasets sourced directly from BigQuery
• Staging Layer: Basic cleaning and standardization: null handling, casting, renaming fields
• Intermediate Layer: Aggregation and joining of related datasets (e.g., sales, attendees, exhibitor metrics)
• Final/Reporting Layer: Business-friendly models used for dashboards - clean, aggregated, and documentation-ready

Key Metrics & Insights

Detailed Findings

• Top Performer: Business Summit 2024 had the highest revenue (£116,633) and profit (£34,050).
• Profitability Discrepancy: Fashion Show 2023 recorded negative profit (-£1,952) despite decent revenue, highlighting a need to revisit cost structures.
• Satisfaction vs. Profit: Tech Expo events delivered high profits and strong feedback, suggesting high-value engagement.
• Regional Differences: Brussels had the highest average satisfaction scores, while Ghent underperformed.
• Temporal Trends: Revenue peaked in January and September, indicating critical planning periods.
• Duration Impact: Longer events (e.g., Fashion Show 2023 at 42 days) did not correlate with better satisfaction or profit.

Interactive Dashboard

The final Tableau dashboard visualizes these insights with interactive filters, enabling stakeholders to explore performance by event, region, and time period. (View on Tableau Public)

Tools Used

Overview

PrimeGroce, a national supermarket chain operating in four U.S. states, needed to optimize its distribution network. With three distribution centers (White Plains, NY; Portsmouth, NH; Augusta, ME) supplying five retail stores (Albany, Burlington, Manchester, Portland, Bangor), the challenge was to minimize daily shipping costs while meeting all store demand and respecting capacity constraints. I developed a comprehensive linear programming model in Excel Solver and explored 9 alternative scenarios to find the most cost-effective distribution strategy.

The Problem

The company required a detailed analysis to determine the optimal daily pallet distribution to each store, minimizing total shipping costs while adhering to:

• Daily demand requirements at each of the 5 retail stores
• Capacity limits at each of the 3 distribution centers (based on labor and truck availability)
• No store can receive more than its daily demand
• No center can ship more than its daily capacity

Key Analytical Tasks & Solutions

Key Outcomes

• Cost Optimization: Achieved minimum daily and annual shipping costs across the entire distribution network
• Strategic Flexibility: Identified that White Plains capacity was non-binding, providing operational flexibility for future growth
• Backhaul Efficiency: Optimized truck utilization with 56 trucks carrying full loads, minimizing empty return trips
• Network Restructuring: Provided data-driven recommendations for potential DC consolidation and capacity expansion
• Route Optimization: Quantified the impact of infrastructure changes (new highway) on distribution costs

Tools Used

Overview

This project aimed to classify body types (e.g., Hourglass, Pear, Rectangle) based on bust, waist, and hip measurements. Since no suitable dataset existed, I generated synthetic data in Python by defining realistic measurement ranges for each body type, creating 100 samples per category. Using this data, I trained a Logistic Regression model and deployed it as an interactive Streamlit web app.

The Challenge

• No existing dataset: Body type classification data was not readily available, requiring synthetic data generation from domain knowledge
• Class overlap: Body type measurements naturally overlap at boundaries, making clean classification difficult
• Accessibility: Needed a simple interface for non-technical users to get instant predictions

The Solution

• Generated synthetic data in Python by defining realistic measurement ranges for each body type (Hourglass, Pear, Rectangle), creating 100 samples per category (300 total)
• Trained a Logistic Regression model achieving approximately 73% accuracy on the synthetic dataset
• Built a clean, user-friendly Streamlit web app where users input their bust, waist, and hip measurements
• App returns an immediate body type prediction with confidence context

Key Results

Future Improvements

• Incorporate additional features (shoulder width, height) for better classification
• Experiment with more advanced algorithms (Random Forest, XGBoost, Neural Networks)
• Collect real-world data to validate and improve the synthetic training set
• Add visual body type illustrations to the app for better user experience

Tools Used

Overview

Built an interactive Streamlit web application that generates complete amortization schedules for loans. Users input principal, interest rate, and term to receive a full breakdown of monthly payments, interest vs. principal allocation, and total interest paid over the life of the loan.

Features

• Interactive Inputs: Principal amount, annual interest rate, and loan term (years) via sliders and number inputs
• Monthly Payment Calculation: Accurate EMI calculation using standard amortization formulas
• Full Schedule Table: Complete month-by-month breakdown showing payment, principal, interest, and remaining balance
• Summary Metrics: Total interest paid, total payment amount, and payoff date
• Data Export: Download the full amortization schedule as CSV
• Responsive Design: Works seamlessly on desktop and mobile devices

How It Works

• User enters loan details: principal, interest rate, and term
• App calculates monthly payment using the amortization formula: M = P × [r(1+r)^n] / [(1+r)^n - 1]
• Generates a complete schedule showing how each payment splits between interest and principal
• Displays summary statistics and allows CSV download of the full table

Key Results

Tools Used

Overview

Designed and developed modern, responsive websites and landing pages for businesses and personal brands. Each project focused on clean UI/UX, mobile-first design, fast load times, and SEO-friendly structure to help clients establish a strong online presence and convert visitors into customers.

What I Build

• Business Websites: Multi-page sites for service-based businesses, portfolios, and agencies
• Landing Pages: High-converting single-page sites for product launches, campaigns, and lead generation
• Portfolio Sites: Personal brand sites for creatives, consultants, and professionals
• E-commerce Frontends: Product showcase pages with cart functionality and payment gateway integration

Technical Approach

• Mobile-First: Every site is designed for mobile devices first, then scaled up to tablet and desktop
• Performance: Optimized images, minified CSS/JS, and lazy loading for fast page speeds
• SEO Ready: Semantic HTML, meta tags, Open Graph tags, and structured data
• Responsive Design: CSS Grid and Flexbox for fluid layouts that work on all screen sizes
• Interactive Elements: Smooth animations, scroll effects, and form validation
• Cross-Browser Compatible: Tested across Chrome, Firefox, Safari, and Edge

Technologies

Need a Website?

I build fast, responsive, and professional websites for businesses and individuals. Whether you need a simple landing page or a full business website, I can help you establish a strong online presence.

Overview

Led agile teams through the full software development lifecycle — facilitating sprint planning, daily standups, retrospectives, and stakeholder reviews. Ensured on-time delivery, removed blockers, and maintained high team velocity across multiple concurrent data and development projects.

What I Do as a Scrum Master / Project Manager

• Sprint Planning: Facilitate sprint goal definition, backlog grooming, and story point estimation
• Daily Standups: Run focused 15-minute syncs to track progress, surface blockers, and align the team
• Retrospectives: Lead continuous improvement sessions to identify what worked, what didn't, and actionable improvements
• Stakeholder Management: Bridge communication between technical teams and business stakeholders
• Backlog Management: Prioritize features, manage scope, and ensure work aligns with business objectives
• Risk Management: Identify, track, and mitigate project risks before they become blockers
• Metrics & Reporting: Track velocity, burndown charts, and delivery predictability
• Team Coaching: Guide teams on agile best practices and remove impediments

Key Achievements

Methodologies & Tools

Background

The HR department of a medium-to-large enterprise sought to use data-driven insights to improve employee performance, satisfaction, and training effectiveness. Management suspected that some employees were being enrolled in multiple training programs with minimal improvements in survey metrics or performance ratings. There were also concerns about demographic or job-type disparities in training outcomes and work-life balance.

The Problem

The organization faced several critical workforce challenges:

• Training ROI uncertainty — no clear visibility into whether training investments were driving performance improvement
• Employee engagement gaps — potential disparities in satisfaction and work-life balance across departments and demographics
• Inequitable development — concerns about training access and outcomes varying by gender, race, or age
• Cost optimization needs — some business units spending heavily on training without seeing measurable performance gains

Business Questions Answered

Dashboard Features

• Interactive Filters: Age group (Young/Mid-Age/Old), Gender (Male/Female) for demographic segmentation
• Employee Cost Analysis: Total employee cost breakdown by employment type (Full-Time, Part-Time, Temporary)
• Work-Life Balance: Department-level scores highlighting areas for improvement (Executive Office, Admin Office, Sales, Software Engineering, Production)
• Training Cost Analysis: Internal vs External training cost comparison
• Engagement by Age: Employee engagement distribution across age groups
• Training Outcomes: Program success rates (Completed/Failed/Incomplete/Passed) with tenure correlation
• Department Satisfaction: Employee count by department and satisfaction level (1-5 scale)
• Demographic Breakdown: Racial diversity distribution (Asian, Black, Hispanic, Other, White)
• Business Unit Performance: Training cost and employee rating by business unit (SVG, TNS, PL, WBL, BPC, NEL, CCDR, MSC, EW, PY2)

Key Metrics

Tools Used

Overview

A complete end-to-end customer retention analysis project using a multi-tool approach — Python for data processing, Tableau for visualisation, and Canva and PowerPoint for the executive presentation layer.

Analysis Scope

• Customer cohort analysis — retention rates by acquisition month
• Churn driver identification — which factors most strongly predict customer loss
• RFM segmentation — Recency, Frequency, Monetary value scoring for all customers
• Tableau dashboard with interactive cohort retention heatmap
• Executive presentation with recommendations for targeted retention campaigns

Tools Used

Background

A large aquatics facility operating across multiple pool sections employs over 600 lifeguard staff across different grades, license types, and specialised roles. Each week, an operations manager manually produced a full 7-day schedule - assigning every staff member to a section, enforcing eligibility rules, managing gender-based shift policies, and tracking days off. The process was time-consuming, error-prone, and difficult to scale as the team grew.

The Problem

The manual scheduling process created several operational pain points:

• Hours lost every weekend - the manager spent significant time building the schedule from scratch each week
• Eligibility errors - staff were occasionally assigned to sections they were not qualified for based on grade or license
• Policy violations - gender-based shift rules and section rotation requirements were hard to enforce manually at scale
• Uneven distribution - staff at the top of the roster were consistently assigned first, leaving those further down under-scheduled
• No rotation tracking - the same staff worked the same sections week after week, with no structured rotation across the 11 sections

The Solution

A fully automated Excel VBA macro was developed that runs directly inside the facility's existing Excel workbook. The manager clicks one button, answers four quick questions, and the schedule is populated automatically — respecting every operational rule the facility follows.

Key Business Rules Automated

• Grade & License Eligibility
• Ladies Night Policy
• Section Rotation using two-pass algorithm
• Breaker Shift Fairness
• Random Processing Order using Fisher-Yates
• Days Off Policy
• Grade Assist Scheduling
• Manual Override Protection

Key Outcomes

Technical Highlights

• Fisher-Yates shuffle algorithm for fair random staff ordering each week
• Two-pass section assignment engine - fresh sections prioritised, least-used repeat as fallback
• Pipe-delimited occurrence tracking for per-staff section usage across the week
• Separate capacity counters for main slots, breaker slots, etc, all pre-loaded from existing manual entries before filling begins
• Runtime prompts replace hardcoded policy flags - training days, Ladies Night, and week start date all configurable at run time
• Full inline VBA comments delivered with the code so the client can make changes independently

Tools Used

Overview

Solved a real client's trading analysis challenge — taking messy, inconsistent trading data and turning it into a clean, structured analysis with actionable findings delivered in a professional report.

What Was Delivered

• Data cleaning — standardised formats, removed duplicates, fixed date and value inconsistencies
• Performance analysis by trading period, asset class, and position type
• Win/loss ratio calculation and trend analysis over time
• Risk-adjusted return metrics calculated using Excel formulas
• Clean, formatted final report with visual summary and key findings

Tools Used