Pavankalyan Adepu
Data Analyst Intern • Middlesbrough, England • a*********@gmail.com • 078*****204 • drivetube.ai/•••••
Career Objective
Data Analyst Intern with 1+ years of experience building ETL pipelines, performing exploratory and financial analysis, and developing interactive Power BI dashboards to inform business decisions. Currently pursuing an MSc in Computer Science (Machine Learning, Big Data & Business Intelligence) at Teesside University. Practical experience includes a Lloyds Banking Group job simulation and multiple end-to-end BI projects using Python, SQL, Power BI and advanced Excel.
Education
Teesside University
MSc Computer Science with Advanced Practice • Middlesbrough, UK • September 2025 – May 2027
Satavahana University
BSc Computer Science • Telangana, India • June 2021 – June 2024
Technical Skills
Programming Languages: Python,VBA
Frameworks and Libraries: Pandas,NumPy,Matplotlib,Seaborn
Databases: SQL,MySQL,Microsoft SQL Server
Data and Analytics: Power BI,Power Query,DAX,Dashboard Development,Data Visualization,ETL,Data Pipeline Management,Data Modelling,Data Wrangling,Exploratory Data Analysis,Statistical Analysis,Segmentation,KPI Development
Tools and Methodologies: Git,GitHub
Spreadsheets & Automation: Microsoft Excel,Pivot Tables,VLOOKUP,XLOOKUP,INDEX MATCH,Macros
Projects
Lloyds Banking Group — Data Analyst Job Simulation | March 2026 – March 2026
Tools Used: Python, Pandas, NumPy, Power BI, DAX
- Analyzed 10,000+ customer records using Python (Pandas, NumPy) to calculate a 20.4% churn rate and identify the top five drivers of attrition across demographic and product segments.
- Segmented customers into four risk tiers and discovered premium/high-income segments churned ~35% above average, informing targeted retention focus.
- Built a Power BI dashboard with six DAX-calculated KPIs to visualise churn, retention, and segment performance for stakeholder review.
- Translated analytical findings into a structured stakeholder report recommending three data-driven retention strategies projected to reduce churn.
- Validated data integrity through cleaning and transformations, ensuring analytic results were reproducible and audit-ready.
Retail and Customer Analytics Project (Personal) | February 2026 – February 2026
Tools Used: Power BI, Power Query, DAX, SQL, Data Modelling
- Modeled four datasets into a star-schema using Power Query to create a clean analytics-ready data pipeline for 12 months of sales data.
- Developed 10+ DAX measures to track AOV, revenue growth, retention and other business KPIs across product and customer dimensions.
- Conducted customer segmentation to identify three high-value segments and two peak seasonal periods, driving targeted marketing recommendations.
- Ensured data integrity with transformation rules and validation checks, preventing double-counting and time-intelligence errors in reports.
- Delivered self-service Power BI dashboards that reduced 3 hours of weekly manual reporting and provided near real-time business insights.
Financial Expense Analysis Project (Personal) | February 2026 – February 2026
Tools Used: Microsoft Excel, Power BI, Power Query, Data Modelling, ETL
- Consolidated 2,500+ transactions totalling €380,000 from three sources into a single audit-ready dataset via an ETL pipeline and standardised 20+ fields.
- Designed eight automated KPI dashboards in Power BI to track departmental spending across five teams and flag overspend trends.
- Identified four overspending trends not visible in raw data, enabling targeted budget adjustments and control measures.
- Automated data transformation and reconciliation steps using Power Query and Excel macros to reduce manual processing time.
- Prepared three structured analytical reports for senior stakeholders summarising spend patterns and recommended cost-saving actions.
Certifications
Data Science Job Simulation — Lloyds Banking Group (Forage) • March 2026
Power BI Data Analytics — Code Basics • March 2026
Advanced Excel for Business Intelligence — Code Basics • January 2026
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