arnoldokoyo630

1. Delivered end-to-end data analysis solutions for international clients across e-commerce, retail, and research domains using SQL, Excel, Python (pandas, NumPy), Tableau, and Power BI.
2. Cleaned, transformed, and analyzed raw datasets containing sales, customer behavior, and operational data to uncover actionable business insights.
3. Designed interactive dashboards in Tableau and Power BI to visualize KPIs such as revenue trends, customer segmentation, and product performance.
4. Wrote complex SQL queries (joins, subqueries, CTEs, window functions) to extract and aggregate data from relational databases for reporting and decision-making.
5. Maintained a 100% job success rate through timely delivery, clear communication, and high-quality analytical outputs.

1. I analyze applicant and institutional data to generate insights for management,
improving recruitment strategies and training outcomes.
2. My role involves drawing actionable trends from repositories and providing data
driven recommendations for effective decision-making.
3. Applied Agile principles, ensuring continuous feedback loops and iterative
improvements.
4. I manage recruitment processes, including collecting, organizing, and updating
applicant data.
5. I ensure candidates are registered in relevant platforms, such as the NITA portal,
and streamline the selection process across various regions.
6. I oversee the institute’s financial reports, advising on income, expenses, and
budget planning through trend analysis.
7. I ensure records are well-maintained and contribute to operational efficiency
through meticulous documentation.

1. Enhanced decision-making efficiency by {20%} by developing data visualizations
for Tata Consultancy Services.
2. Enhanced client engagement, resulting in a {20%} increase in satisfaction by
preparing {15} strategic meeting questions.
3. Created visuals for data analysis to help executives with effective decision making.

1. Enhanced commercial insights accuracy by {15%} through data analytics job
simulation for data science team.
2. Increased data-driven recommendations profitability by {15%} through expertise in
data preparation and customer analytics.
3. Extended analytical capabilities to identify benchmark stores for conducting uplift
testing on trial store layouts, enabling evidence-based decision-making.
4. Leveraged acquired data analytics and insights from previous tasks to create
comprehensive reports for the Category Manager, facilitating informed strategic
decisions and enhancing commercial applications.
5. Monitored key product performance indicators such as daily active users (DAU),
monthly active users (MAU), and conversion rates to assess feature adoption and
user engagement across digital platforms.