Data analyst / Riyadh, Saudi Arabia

Turning complex data into clear business decisions.

I’m Adil, an early-career analyst who connects data, business context, and thoughtful visualisation to help teams see what to do next.

Open to Data Analyst & Business Analyst roles

Explore my work Download résumé

From a question worth asking to an answer a business can use.

01 / About me

Curious about the why behind the numbers.

I’m a Computer Science and Engineering graduate from Lovely Professional University, now building my career in data and business analytics. I enjoy taking an open-ended business question, finding the right data, and turning the analysis into a clear next step.

My projects span retail business intelligence, customer churn, sales forecasting, and predictive modelling. I work across the analysis lifecycle: cleaning and validating data with Python and SQL, exploring patterns, then communicating useful findings through Power BI, Tableau, and visualisations.

My approach is practical and evidence-led: understand the decision first, check the quality of the data, make the reasoning easy to follow, and keep recommendations grounded in what the analysis can actually support. I’m currently based in Riyadh on a Transferable Iqama and looking for an entry level analyst role where I can learn from a team and contribute from day one.

Beyond the dashboard
Interested in customer behaviour, business strategy, and making data easier to act on.

02 / Selected work

Projects with a business question at the centre.

A selection of analytics work, presented from newest to oldest.

Business intelligenceJanuary 2026

Retail Business Intelligence & Analytics System

Built an end-to-end analysis of 400K+ retail records, combining Python, SQL, and Power BI to explore revenue, customer behaviour, churn, and sales trends.

400K+rows analysed £8.83Mrevenue surfaced

View project on GitHub

Exploratory analysisFebruary 2025

Retail Sales & Movie Industry Analysis

Explored Walmart sales alongside temperature, holidays, and CPI to identify potential revenue drivers and risks; separately examined how movie genre, runtime, budget, and audience engagement relate to box-office performance.

View project on GitHub

Predictive modellingFebruary 2025

Student Outcomes & Vehicle Purchase Prediction

Applied logistic regression to student exam outcomes and linear regression to hybrid vehicle purchase data, with preprocessing, model evaluation, and exported predictions.

View project on GitHub

Interactive dashboardJanuary 2025

Sales & Profit Dashboard

Created an interactive Power BI dashboard to track revenue and profit. Slicers, Q&A, key influencers, and a decomposition tree help people explore performance from different angles.

View project on GitHub

Sales analyticsNovember 2024

AtliQ Sales Insights Dashboard

Connected Tableau to MySQL data to present sales trends, top products, and customer demographics in a dashboard designed to make business patterns easier to explore.

View project on GitHub

03 / Case studies

The question, the work, and what it can inform.

A closer look at the business thinking behind selected projects.

Retail business intelligence · January 2026

Making a large retail dataset useful for decisions

Business problem: Understand sales performance and customer behaviour across a large retail dataset.
Method: Cleaned, analysed, and validated 400K+ rows with Python and SQL, then built Power BI views with forecasting and churn analysis.
Finding: The analysis surfaced £8.83M in revenue alongside patterns in customer behaviour and sales trends.
Recommendation: Use the dashboard to investigate churn and sales patterns by customer and period before prioritising retention or growth actions.

Explore the project

Sales performance · January 2025

Giving sales and profit more than one point of view

Business problem: Make revenue and profit performance easier to monitor and investigate.
Method: Built a Power BI dashboard with several chart types, interactive slicers, Q&A, key influencers, and a decomposition tree.
Finding: The interactive views give users ways to examine performance and explore contributing factors; the CV does not report specific quantified findings.
Recommendation: Use the exploration features to follow up on changes in revenue and profit, then validate possible drivers against the underlying business context.

Explore the project

Retail sales exploration · February 2025

Exploring what moves retail sales

Business problem: Explore how Walmart sales trends compare with external factors such as temperature, holidays, and CPI.
Method: Used Python, Pandas, Seaborn, and Matplotlib for exploratory analysis and created an interactive dashboard.
Finding: The analysis was designed to identify potential risks, growth opportunities, and revenue drivers; no numerical effects are claimed in the CV.
Recommendation: Use the exploratory patterns to guide deeper investigation, and test candidate drivers against additional periods and relevant business context.

Explore the project

04 / Skills & tools