Praveen Yeduresi
Data Analyst • Glasgow, United Kingdom • p**************@gmail.com • +44*******384 • drivetube.ai/•••••
Professional Summary
Data Analyst with 5+ years of experience delivering analytics and BI solutions across banking, academic and public-sector domains. Skilled in SQL and Python (pandas, NumPy) to prepare and model large transactional and operational datasets, and in scikit-learn for predictive modelling. Experienced building end-to-end analytics on Microsoft Fabric and Databricks, and creating actionable Power BI and Tableau dashboards with DAX to support risk, procurement and operations decisions. Proven track record designing fraud-detection models, customer propensity models and forecasting pipelines, and operationalising them into scheduled ETL jobs and stakeholder-facing reports. Comfortable owning data ingestion, transformation, model development and dashboard delivery to drive measurable business outcomes.
Technical Skills
Programming Language: Python,SQL
Databases: Microsoft Fabric
Cloud Platforms: Microsoft Azure
Version Control & Development Tools: Git,GitHub
Data Engineering & Processing: PySpark
Data Analysis & Visualization: Pandas,NumPy,Power BI,DAX,Regression,A
Machine Learning & AI: Clustering
AI/ML Frameworks & Libraries: scikit-learn
Generative AI & LLMs: LangChain,Generative AI,Gemini
Vector Databases & RAG: Chroma,RAG
Work Experience
Lloyds Banking Group
United Kingdom
Data Analyst
06/2025 – Present
Worked at a major UK retail bank building fraud detection and customer analytics platforms to support risk, compliance and marketing teams.
Tech Stack: SQL, Python, pandas, NumPy, scikit-learn, Power BI, Microsoft Fabric
- Architected an end-to-end fraud detection analytics platform, integrating transactional and behavioural datasets using SQL to provide a single risk-monitoring layer.
- Developed predictive fraud models using Python and scikit-learn to surface high-risk activity, improving early fraud detection by 35%.
- Performed exploratory data analysis with pandas and NumPy to identify anomalous patterns and inform feature engineering for models.
- Built interactive Power BI dashboards and authored DAX measures to visualise fraud KPIs and alert trends for risk and compliance stakeholders.
- Integrated customer and transaction sources into a Microsoft Fabric Lakehouse to create a unified analytics layer for cross-sell modelling.
- Implemented scheduled Python-based scoring jobs to operationalise propensity models and publish outputs to Fabric-driven data warehouse.
KL University
India
Data Analyst
02/2023 – 05/2023
Delivered academic operations analytics and automated reporting to support enrolment, faculty workload and resource planning.
Tech Stack: Databricks, PySpark, Python, Power BI, Azure SQL
- Built Databricks ETL pipelines using PySpark to clean and transform student, course and enrolment data from multiple systems.
- Automated data preparation workflows with Python scripts and scheduled Databricks jobs, reducing manual effort by 35%.
- Developed Power BI dashboards to visualise enrolment trends, faculty workload and resource utilisation for academic operations.
- Defined KPIs with stakeholders and implemented them in dashboards to align reporting with institutional objectives.
- Optimised SQL queries against Azure SQL to improve ETL job performance and reduce pipeline runtime.
- Documented data lineage, pipeline schedules and handover notes to improve maintainability and support the BI team.
Department of Horticulture, State Government of India
India
Data Analyst
10/2018 – 01/2023
Supported public-sector analytics for customer behaviour, retention forecasting and procurement price analysis to inform policy and procurement decisions.
Tech Stack: SQL, Python, scikit-learn, Power BI, Azure SQL
- Cleaned and transformed large administrative and procurement datasets using SQL and Python to produce reliable analytical datasets for teams.
- Conducted customer segmentation and trend analysis using clustering techniques in scikit-learn to identify behaviour drivers.
- Developed churn prediction models using logistic regression in Python, reducing observed churn by 15%.
- Built Power BI dashboards to visualise procurement price trends, seasonal patterns and category-level insights for procurement planners.
- Ingested historical procurement and seasonal datasets into Azure SQL and prepared structured datasets for forecasting workflows.
- Performed time-series decomposition and collaborated with the data science team to improve forecast accuracy and model inputs.
- Implemented SQL-based data validation rules and checks to improve data accuracy and reduce downstream rework.
Projects
Q&A System with Retrieval-Augmented Generation (RAG)
Tools Used: Python, LangChain, Chroma, Google Generative AI Gemini, PyPDF, RAG
- Consolidated research papers and technical documents into a Chroma vector store using LangChain to enable efficient semantic retrieval.
- Implemented document embedding and similarity search pipelines in Python and validated retrieval precision for question answering.
- Deployed a scalable RAG pipeline that parsed PDFs (PyPDF), indexed documents and served retrieval responses through Gemini-based generative models.
Education
University of Essex, Colchester, UK
MSc Data Science and its applications – Merit (69%) • Colchester, UK • 01/2024 – 01/2025
Certifications
Microsoft Certified: Azure Data Scientist Associate — Microsoft
Databricks Accredited: Generative AI Fundamentals & Lakehouse Fundamentals — Databricks
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