Machine Learning Data Analyst

Divya Sri Kesireddy

***********@*****.*** 630-***-**** Chicago, IL linkedin

Professional Summary

Results-driven Data Analyst and Machine Learning Engineer with 6+ years of experience building scalable, data-driven solutions across healthcare, life sciences, and financial domains. Adept at transforming complex datasets into predictive insights using Python, SQL, and AWS AI/ML services. Skilled in data modeling, ETL design, and statistical/machine learning techniques such as regression, classification, clustering, and anomaly detection to drive business decisions and operational optimization. Experienced in deploying and monitoring models using AWS SageMaker, integrating predictive outputs into Power BI and Tableau dashboards, and ensuring data quality and governance across full-stack data pipelines. Recognized for delivering actionable insights, reducing reporting latency, and applying AI-powered analytics to enhance enterprise performance.

Technical Skills

Programming & Analytics: Python (pandas, numpy, scikit-learn, matplotlib, seaborn), R, SQL, PL/SQL, PySpark

Machine Learning & AI: Regression, Classification, Clustering, NLP, Feature Engineering, Model Evaluation (ROC, AUC, SHAP), Hyperparameter Tuning, Predictive Analytics, Time Series Forecasting

ML Frameworks & Tools: AWS SageMaker, TensorFlow, Keras, XGBoost, MLflow, Jupyter Notebook, Docker

Data Engineering & ETL: Informatica PowerCenter, AWS Glue, Redshift, Lambda, SQL Server Integration Services (SSIS)

Databases: Oracle, SQL Server, Teradata, MySQL

BI & Visualization: Power BI, Tableau, Looker, Excel (advanced formulas, pivot tables)

Data Modeling Tools: ERwin, Power Designer

Statistical & Analytical Methods: Hypothesis Testing, Correlation, ANOVA, Regression, EDA

Cloud & DevOps: AWS (S3, Redshift, Lambda, EC2), Git, CI/CD, Docker

Others: MS Project, TOAD, MS Office, UNIX/Windows

Professional Experience

Data Analyst

Abbott Laboratories, Abbott Park IL August 2023 – Present

Collaborated with enterprise-wide groups including senior management and technology leaders to support migration from legacy systems to modern data warehouse solutions.

Designed and optimized data models using Power Designer to ensure data integrity across transactional and reporting systems.

Developed interactive Power BI and Tableau dashboards to visualize mortgage and loan performance, enabling business units to track KPIs, compliance, and customer behavior in real time.

Created detailed data dictionaries, mappings, and ERDs, facilitating smoother ETL workflows and reducing rework by 20%.

Conducted JAD sessions with business stakeholders to expand databases, clarify requirements, and align data models with evolving business needs.

Performed exploratory data analysis (EDA) on large datasets to uncover business trends, anomalies, and new opportunities for improving compliance and customer engagement.

Designed and implemented predictive models using Python (scikit-learn) to forecast loan performance and compliance outcomes, improving decision accuracy by 18%

Automated model training, tuning, and evaluation pipelines using AWS SageMaker and Lambda, accelerating experimentation and deployment cycles.

Applied statistical methods (correlation, regression, variance analysis) to support data-driven decision-making for business leaders.

Integrated predictive insights into Power BI dashboards, enabling real-time KPI forecasting and proactive risk management.

Developed detailed documentation for analytics workflows, including SQL scripts, data cleaning steps, and KPI definitions, ensuring repeatability and transparency.

Optimized SQL queries, indexes, and stored procedures, reducing report generation time by 30%.

Environment: Power BI, Tableau, SQL Server, Oracle, Power Designer, Python, Excel, Windows, MS Project, TOAD

Data Analyst

CVS Health Northbrook, IL January 2023- July2023

Partnered with pharmacy and healthcare operations teams to design and develop analytical dashboards in Power BI and Tableau, improving visibility into prescription trends, patient adherence, and operational KPIs.

Extracted and transformed large-scale healthcare data from SQL Server and Teradata environments, ensuring data consistency and integrity across multiple business units.

Designed data models and developed ETL mappings to integrate disparate datasets (claims, prescriptions, and patient records) into unified reporting views.

Created and maintained complex SQL queries, stored procedures, and performance-tuned scripts to support recurring data validation and ad hoc analysis.

Conducted exploratory data analysis (EDA) to identify gaps in medication adherence and potential cost-saving opportunities for pharmacy benefit programs.

Collaborated with cross-functional teams to define KPIs, data quality checks, and governance processes for clinical and financial reporting.

Developed and documented data dictionaries, lineage diagrams, and business logic mappings, enabling faster on boarding and improved transparency across analytics workflows.

Applied statistical techniques (regression, correlation analysis) to assess relationships between medication usage and patient outcomes, supporting healthcare strategy optimization.

Developed and validated machine learning models (logistic regression, random forest) to predict medication non-adherence and patient risk categories.

Deployed models within AWS SageMaker and automated report generation through Power BI APIs, improving operational efficiency by 30%.

Implemented feature engineering and data preprocessing workflows in Python for large-scale healthcare data, enhancing model performance.

Contributed to migration projects transitioning on-premises reporting systems to cloud-based data platforms, ensuring minimal disruption to business continuity.

Environment: Power BI, Tableau, SQL Server, Teradata, Excel, Python, Power Designer, Windows, MS Office

Data Analyst

Mayo Clinic Hyderabad, India June 2018 – November 2021

Supported the redesign of enterprise data warehouse systems for credit and debit card transaction processing, ensuring compliance with strict data governance and security policies.

Utilized Tableau to create executive-level dashboards, enabling senior leadership to perform ad-hoc analysis of customer transactions, credit risk, and fraud patterns.

Designed and maintained fact and dimension tables to support advanced reporting needs, following Ralph Kimball methodologies.

Partnered with cross-functional teams to gather requirements, translate them into technical data models, and deliver scalable warehouse solutions.

Implemented ETL workflows in Informatics to load cleansed data into marts, improving downstream analytics accuracy.

Conducted statistical analysis on transaction data to identify fraud patterns, assess credit risk, and quantify operational improvements.

Built fraud detection and anomaly-detection models using Python (scikit-learn, XGBoost), reducing false positives by 15%.

Designed time-series forecasting models to predict patient billing and transaction volume trends, optimizing resource allocation.

Deployed trained models on AWS EC2/SageMaker for near real-time analytics and integrated results into Tableau dashboards.

Utilized Python for data cleaning and statistical analysis, complementing SQL queries to provide deeper insights into large datasets.

Documented data processes, lineage, and analytical approaches to ensure accuracy, reproducibility, and compliance with governance policies.

Authored complex PL/SQL scripts and automated reporting queries, reducing manual reporting effort by 40%.

Delivered near real-time dashboards for credit arrangements and billing cycles, accelerating decision-making and improving customer service.

Environment: Tableau, Informatics Power Center, Oracle, SQL Server, Python, PL/SQL, Windows, UNIX

Certifications

Microsoft Certified: Data Analyst Associate (Power BI) – Microsoft

Tableau Desktop Certified Data Analyst – Tableau

Informatica Power Center Developer Certification – Informatica

AWS Certified Data Analytics – Specialty – Amazon Web Services

Google Data Analytics Professional Certificate – Coursera

Education

Master's in computer science from Campbellsville University – Louisville, Kentucky

Bachelor's in computer science and engineering from JNTUH – Hyderabad, India

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