Data Analyst Business Intelligence

Lokesh Miriyala Data Analyst

*****************@*****.*** 959-***-**** USA LinkedIn

Summary

Detail-oriented Data Analyst with 3+ years of hands-on experience in data preprocessing, statistical analysis, and business intelligence. Proficient in Python, SQL, cloud platforms, and data visualization tools to transform complex data into actionable insights. Skilled in building scalable ETL pipelines, performing anomaly detection, and supporting data-driven decision-making. Strong analytical and problem-solving abilities with a focus on accuracy, efficiency, and continuous improvement in data processes across diverse business environments. Technical Skills

• Programming & Scripting: Python (Pandas, NumPy, SciPy, Statsmodels), R (dplyr, ggplot2), SQL (CTEs, Window Functions), Bash

• Data Management & Databases: PostgreSQL, SQL Server, AWS Athena, Azure Data Lake, Azure Synapse

• Cloud & Big Data Technologies: AWS Glue, AWS Lambda, AWS Glue DataBrew, Amazon S3, Azure Cloud Services

• ETL & Data Engineering: Data pipeline development, real-time data processing, schema consistency checks, data validation automation, feature engineering

• Data Analysis & Statistics: Exploratory Data Analysis (EDA), hypothesis testing, multivariate analysis, anomaly detection (Z-score, clustering), cohort analysis, RFM modeling, variance analysis

• AI/ML & Predictive Analytics: Unsupervised learning (K-Means, DBSCAN), regression models, statistical modeling, pattern recognition, clustering algorithms, predictive modeling for segmentation and forecasting

• Data Visualization & Reporting: Power BI (custom DAX), Tableau (Azure integration), Excel (VLOOKUP, IFERROR, pivot tables), interactive dashboard development

• Tools & Methodologies: Git, Agile methods, data auditing, model input validation, cross-functional collaboration, business requirement analysis Professional Experience

Data Analyst, TD Bank 10/2024 – Present Remote, USA

• Worked on Customer Segmentation for Personalized Banking Offers. Implemented customer segmentation using unsupervised ML models (e.g., K- Means, DBSCAN) on behavioral and demographic data, enhancing offer personalization and increasing targeted campaign response rates by 25% through agile cross-functional collaboration.

• Developed optimized ETL workflows via AWS Glue and executed high-performance SQL on Athena to process 90% of parquet-based datasets, reducing query latency by 40% and supporting real-time ML feature pipelines.

• Applied Python (Pandas, NumPy, Statsmodels) to preprocess 80% of transaction data, engineered model-ready features, and performed multivariate statistical testing to support ML-driven segmentation and prediction initiatives.

• Executed advanced cohort and RFM analysis on customer lifetime value using regression and clustering models, identifying high-value segments that contributed to an 18% uplift in customer retention through tailored product strategies.

• Used AWS Glue DataBrew and Lambda to automate 95% of data validation, anomaly detection, and schema consistency checks, ensuring 99% model input accuracy and reducing ML model failure rates.

• Built interactive Power BI dashboards with custom DAX measures and predictive visual analytics, delivering campaign performance insights and improving strategic decisions by 30% across customer targeting and retention teams. Data Analyst, Acentra Health India Pvt Ltd 01/2022 – 07/2023 Chennai, India

• Collaborated with compliance and audit teams to analyze healthcare insurance claims and detect fraud. Led requirement gathering, reducing false fraud alerts by 23% across provider networks in Tamil Nadu through refined detection criteria.

• Extracted structured claims data using SQL Server from Azure Data Lake and PostgreSQL. Used CTEs and Window Functions to process over 4 million records, identifying abnormal claim frequencies and suspicious billing behaviors.

• Conducted segmentation and trend analysis to spot duplicate billing and expensive procedures. Enabled the detection of 12% high-risk claims, reducing investigation time and improving compliance during quarterly internal healthcare audits.

• Utilized Python libraries like Pandas, NumPy, and SciPy for data preprocessing. Applied Z-score for anomaly detection and K-Means clustering to identify hidden fraud clusters within an 18-month historical claim dataset.

• Performed provider-level claim analysis using R functions like mutate and summarise . This variance analysis uncovered 5 non-compliant providers who exhibited a 30% overbilling rate compared to peer benchmarks.

• Used Excel for claim validation with VLOOKUP and IFERROR, ensuring 98% record accuracy. Designed Tableau dashboards integrated with Azure Synapse for real-time fraud visualization, cutting reporting time by 35%. Data Analyst, Acentra Health India Pvt Ltd 01/2021 – 12/2021 Chennai, India

• Supported senior analysts by extracting and cleaning healthcare claims data using SQL and Excel. Helped identify anomalies and patterns, contributing to a 23% reduction in false fraud alerts across Tamil Nadu provider networks.

• Assisted in building Tableau dashboards and performing basic statistical analysis using Python and R. Visualized regional fraud trends and provider behaviors, reducing reporting time by 35% and improving data accuracy for audits. Education

Master of Science in Computer Science 08/2023 – 04/2025 Rivier University - Nashua, NH, USA

Bachelor of Engineering in Electronics and Communication Engineering 08/2019 – 05/2023 Bharath Institute of Higher Education and Research - Chennai, India

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