Data Analyst

PREMI KALIKIRI

Data Analyst

Ohio, USA Mobile No: 380-***-**** Email: *****************@*****.***

SUMMARY

Data Analyst with around 5 years of experience in healthcare and finance domains, specializing in transforming raw data into actionable insights that drive operational efficiency, risk mitigation, and improved decision-making.

Proficient in data analysis tools and programming languages including SQL, Python (Pandas, NumPy, Scikit-Learn), R, and SAS for advanced statistical modeling, predictive analytics, and trend analysis.

Expertise in data visualization and reporting using Power BI, Tableau, and QlikView, delivering interactive dashboards and KPIs for stakeholders across clinical, compliance, and financial teams.

Hands-on experience with data management, ETL processes, and cloud platforms (AWS, Azure, GCP), ensuring secure, scalable, and regulatory-compliant data integration and reporting.

Strong background in EHR systems, patient outcome analytics, credit risk modeling, fraud detection, and compliance reporting, leveraging data-driven strategies to optimize business and healthcare outcomes.

SKILLS

Programming Language:

Python, SQL, R

Packages:

NumPy, Pandas, Matplotlib, SciPy, Scikit-learn, TensorFlow, Seaborn, ggplot2

Visualization Tools:

Tableau, Power BI, Advanced Excel (Pivot Tables, VLOOKUP)

Cloud Technologies:

AWS (EC2, S3, Redshift, Athena, Glue, DynamoDB), Azure, Snowflake

Database:

MySQL, PostgreSQL, MongoDB, SQL Server

Other Technical Skills:

Machine Learning Algorithms, ETL Tools, Statistics, ServiceNow, SSIS, SSRS, MapReduce, Snowflake, Alteryx, ANOVA, Hypothesis Testing, Regression Analysis, Data Visualization, Data warehousing, Data transformation, Data Storytelling, Business Analysis, Regression, A/B Testing, Forecasting & Modelling, Data Cleaning, Data Wrangling, Informatica, Jira, FRD BRD, Gap Analysis, Cost Benefit Analysis, Risk Analysis, UAT, JAD, Git, GitHub

Soft Skills

Time Management, Leadership Management, Problem-solving, Negotiation, Decision-Making, Documentation and Presentation, Verbal Communication

WORK EXPERIENCE

Ohio Health, Ohio Data Analyst Aug 2024 - Present

Extracted, cleaned, and analyzed large volumes of healthcare claims data using SQL and Snowflake, ensuring accuracy and improving data retrieval speed by 30%.

Developed automated reports in Excel (VBA, Pivot Tables, Macros) and Power BI, reducing manual effort by 40% and enhancing reporting efficiency.

Performed claims trend analysis using Python (Pandas, NumPy, Matplotlib) to identify patterns in denials, reimbursements, and provider performance, leading to data-driven process improvements.

Designed interactive dashboards in Tableau, visualizing key metrics such as claims processing time, approval rates, and revenue impact, enabling stakeholders to make faster, data-driven decisions.

Conducted root cause analysis of claim rejections and discrepancies using SQL, Excel, and Snowflake queries, identifying inefficiencies and implementing corrective measures that increased claims approval rates by 20%.

Defined business requirements by collaborating with stakeholders, healthcare providers, and data engineers, ensuring alignment between business needs and data solutions.

Created and optimized complex SQL queries for claims tracking, fraud detection, and anomaly detection, reducing billing discrepancies by 25% and improving revenue cycle efficiency.

Automated data validation and quality checks using Python, Pandas, and Excel, ensuring compliance with HIPAA regulations and minimizing claims processing errors.

Developed and presented actionable insights through Excel dashboards, Tableau visualizations, SQL/Snowflake reports, and PowerPoint presentations, influencing strategic decisions and improving claims management workflows.

Infosys, India Data Analyst Jan 2020 – Jul 2023

Project: 1

Designed and implemented profitability models and credit risk segmentation frameworks using Python (Pandas, NumPy), R (logistic regression, clustering), and SAS, improving risk model precision by 18% and strengthening lending decision accuracy.

Engineered advanced SQL queries (Teradata, PostgreSQL, Oracle) to extract and validate multi-source transactional, behavioral, and demographic datasets, reducing data preparation cycle time by 30%.

Developed customer lifetime value (CLV) models and delinquency trend analyses to assess default risk and payment behaviors, enabling credit policy teams to proactively adjust lending strategies.

Built predictive risk scoring algorithms in SAS for portfolio-level stress testing and regulatory compliance (CCAR, Basel), enhancing compliance reporting efficiency by 25%.

Created interactive Tableau dashboards to monitor KPIs such as charge-off rates, delinquency buckets, and portfolio profitability, providing executives with real-time decision-making insights.

Automated recurring credit risk and profitability reporting pipelines with Python and Snowflake, cutting manual reporting time by 20+ hours per month.

Collaborated with risk analysts, credit policy managers, and data engineers in an Agile delivery model (Jira, Git) to ensure model outputs aligned with compliance and business objectives.

Conducted comprehensive data quality checks, fraud trend analysis, and root cause investigations, strengthening data integrity for regulatory reporting and model accuracy.

Designed Excel-based financial models with advanced macros for quick what-if scenario analysis, improving risk-adjusted return forecasts by 15%.

Partnered with compliance and finance teams to align risk models with Basel III and CCAR reporting requirements, ensuring regulatory adherence and audit-readiness.

Segmented customer cohorts based on behavioral and profitability clusters, helping the portfolio team identify high-value, low-risk customers, resulting in a 12% uplift in cross-sell opportunities.

Delivered executive-level insights through automated self-service dashboards, improving portfolio monitoring efficiency and enabling senior leadership to reduce delinquency exposure by 10% year-over-year.

Project: 2

Designed and optimized SQL queries to extract, clean, and transform large-scale retail sales, customer, and inventory datasets from relational databases, ensuring 100% accuracy and improving reporting efficiency by 25%.

Developed interactive dashboards and reports in Tableau, delivering real-time insights on customer purchase patterns, seasonal sales trends, and product performance, which supported data-driven decisions for marketing and merchandising teams.

Applied Python (Pandas, NumPy) for advanced data analysis, including customer segmentation, demand forecasting, and churn analysis, leading to a 15% increase in targeted campaign effectiveness.

Collaborated with cross-functional teams (business stakeholders, data engineering, and IT) to define KPIs, automate recurring reporting processes, and implement data quality checks, reducing manual effort by 30% and improving overall data governance.

EDUCATION

University of Cincinnati,

-Master of Science in Information Technology

Jawaharlal Nehru Technological University Hyderabad, India:

-Bachelor of Technology in Computer Science

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