John Doe

Data Analyst

ABOUT

Brief introduction

As a skilled data analyst with expertise in SQL, Excel/Google Sheets, and Python (Pandas, Scikit-learn), I thrive on turning raw data into actionable insights. With experience in Google Analytics, Power BI, and machine learning tools like TensorFlow, I drive data-driven solutions for complex business problems.

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SKILLS & EXPERTISE

Our Expertise to build Your Skills

SQL Excel Google Sheets Python (Pandas, Scikit-learn) Google Analytics
Data Analyst Tensor Flow, Excel, Power BI, Machine learning, LLM, AI Tools

January 2021 –January 2022

Data Analyst

PWC

Here’s a breakdown of the information:

- Built data and reporting infrastructure from the ground up using Tableau and SQL to provide real-time insights into product performance, marketing funnels, and business KPIs.
- Designed and implemented A/B experiments for products, reducing churn by 12 basis points.
- Implemented a long-term pricing experiment that improved customer value by 25%.
- Built operational reporting in Tableau to identify areas of improvement for contractors, resulting in $250K in annual incremental revenue.
- Led a team of 2 full-time employees and 4 contractors.

January 2022 –January 2023

Data Analyst /

Barclays

- Partnered directly with the executive team as the first data hire.
- Formulated and reported on KPIs across web properties that received 225M visitors annually.
- Utilized SQL and Google Sheets for data analysis and reporting.
- Built a logistic regression model to assist the SEO team in selecting keywords, resulting in a 15% year-over-year increase in site visitors in 2018.
- Collaborated with product managers to perform cohort analysis.
- Identified an opportunity to reduce pricing by 22% for a specific user segment, increasing yearly revenue by $730K.
- Developed root cause reports to address issues with customer conversions.
- Revealed insights from the reports that led to a 32% boost in conversions.

January 2023 - January 2024

Data Science intern

The Skillians

- Developed and owned reporting for a nationwide retention program using Python, SQL, and Excel, resulting in saving approximately 90 hours of monthly labor.

- Identified procedural areas of improvement through customer data analysis, using SQL to increase the profitability of a nationwide retention program by 8%.
- Applied predictive models and data analysis to understand and forecast repair costs for vehicles in the market.
- Presented findings on repair cost predictions to stakeholders for informed decision-making.