Data Analyst - SQL, Python, Tableau, Power BI, AWS

HRISHIKESH KATHIKAR

United States **********@*****.*** 908-***-**** LinkedIn

PROFESSIONAL SUMMARY

Data Analyst with experience in reporting operations, dashboard development, data validation, and workflow automation across healthcare, finance, and operational analytics. Proficient in SQL, Python, Tableau, Power BI, and AWS/cloud-based data workflows, with expertise in large dataset analysis, ETL processes, KPI reporting, data quality checks, and process optimization. Skilled at translating complex data into actionable insights, automating reporting pipelines, and improving operational eƯiciency. WORK EXPERIENCE

Serenity Senior Care LLC Full-Time Bloomfield, NJ Data Analyst Apr 2025 – Present

Analyzed 15,000+ patient care records and scheduling data using Python, SQL, and Excel to identify trends in care delivery, staƯing eƯiciency, and patient outcomes.

Conducted predictive analysis on patient admissions and service demand, improving staƯing forecasts by 20% for peak periods.

Maintained reporting schedules and supported cross-functional teams in meeting data delivery milestones.

Cleaned, normalized, and maintained daily operational datasets from electronic health records (EHR) and booking systems to ensure data accuracy of 98%+.

Performed trend analysis on patient feedback and caregiver performance, identifying 5 actionable areas for service quality improvement.

Managed recurring operational reporting workflows, ensuring accurate and timely delivery of KPI and performance reports to internal stakeholders.

Collaborated with nursing managers and operations teams to translate analytical insights into actionable strategies for care improvement and cost optimization.

DataArt Internship New York, NY

Data Science Intern Aug 2024 – Oct 2024

Cleaned, transformed, and analyzed 50,000+ structured records using Python, SQL, Excel, and Pandas, improving data accuracy and supporting business reporting initiatives.

Conducted exploratory data analysis (EDA) on customer and operational datasets, identifying 5+ key trends and performance patterns used to support decision-making.

Supported the development of machine learning models using scikit-learn, contributing to predictive analysis with model accuracy improvements of up to 12% during testing.

Automated recurring data cleaning and reporting workflows using Python, reducing manual reporting time by approximately 30%.

Developed Tableau dashboards to visualize portfolio risk metrics and financial performance indicators for stakeholder reporting.

British Airways Internship Remote

Data Analyst Intern Jan 2024 – Mar 2024

Performed sentiment analysis on 20,000+ customer reviews using Python (NLTK, Pandas), uncovering 5 key service improvement areas that informed operational enhancements.

Cleaned, normalized, and structured 50,000+ transactional and booking records, enabling forecasting of seasonal revenue trends with 95% data accuracy.

Created interactive KPI dashboards in Power BI, visualizing on-time performance vs. operational cost trends for executive- level decision-making.

Conducted trend analysis on flight delays, cancellations, and customer complaints, identifying patterns to support operational eƯiciency initiatives.

Assisted in developing predictive models for flight demand and revenue forecasting, improving planning accuracy by 10– 12%.

Automated weekly performance and revenue reports using Python scripts, reducing manual reporting eƯort by 30%. PROJECTS

Financial Performance Dashboard for Retail Chain

Designed and deployed an end-to-end financial analytics dashboard in Excel and Power BI integrating 24 months of historical data from 12 retail stores, including sales, expenses, and profit & loss statements.

Developed ETL pipelines with Power Query for automated data cleaning, transformation, and refresh schedules, eliminating manual intervention in monthly reporting.

Implemented DAX measures and calculated columns to compute KPIs such as Gross Margin %, Net Profit %, Operating Ratio, and Year over-Year (YoY) Growth.

Applied trend analysis and variance analysis to identify underperforming stores, leading to actionable recommendations that reduced losses and improved net profitability by ~$150K annually. Investment Portfolio Risk Analysis

Built a Python-based portfolio risk management model leveraging Monte Carlo simulations (10,000+ iterations) to evaluate the risk return trade-oƯ of a $500K multi-asset portfolio.

Computed financial risk metrics such as Sharpe Ratio, Beta, Value-at-Risk (VaR), and Maximum Drawdown, benchmarking portfolio performance against the S&P 500 index.

Delivered data-driven recommendations to optimize asset allocation strategies and mitigate downside risk exposure by 12%.

Automated simulation scripts for continuous what-if scenario testing, enabling investors to stress-test portfolios under market volatility conditions.

Built/hosted data workflows using AWS cloud services for scalable data processing and storage. Credit Risk Scoring Model

Developed a supervised machine learning model in Python using Logistic Regression, Random Forest, and Gradient Boosting to predict default probability on a historical loan dataset containing 50,000+ customer records.

Achieved 86% accuracy and an AUC of 0.91, significantly outperforming baseline rule-based models.

Deployed a Tableau dashboard to visualize portfolio-level metrics including delinquency rate, recovery rate, and Expected Loss, enabling risk oƯicers to monitor real-time credit exposure.

Implemented confusion matrix and ROC analysis for model validation, ensuring compliance with financial risk management standards.

Health Analytics on OCD Patients

Built an interactive Power BI dashboard to analyze clinical data from 1,500 OCD patient records, visualizing treatment eƯectiveness across demographics (age, gender, ethnicity).

Designed SQL queries on relational databases to generate statistical summaries and cross-tab reports on symptom severity vs treatment outcomes.

Applied ANOVA and correlation analysis in Python (SciPy, Pandas) to identify statistically significant factors influencing recovery rates.

Enabled researchers to identify key intervention gaps by comparing symptom severity distributions across therapy methods.

Contributed results for academic publication, showcasing data-driven insights into mental health treatment eƯectiveness. EDUCATION

PACE University – Seidenberg School of Science New York, NY Master of Science in Data Science – CGPA:3.4 Jan 2023 – Dec 2024 Vignana Bharathi Institute of Technology Hyderabad, India Bachelor of Technology in Computer Science Aug 2018 – Aug 2022 SKILLS

Financial Analysis Tools: Excel (Pivot Tables, Power Query, Financial Modelling), PowerBI, DAX, KPI Tracking, Tableau Programming and Data: Python (Numpy, Pandas, Matplotlib, Seaborn, Scikit-learn, Keras, Tensorflow), SQL (MySQL, Postgres) Data Science and Analytics: Statistical Analysis, Machine Learning, Predictive Modelling, Monte Carlo Simulation, Time Series Forecasting, Apache Airflow

Web and UI: HTML/CSS, JavaScript, React.js, UI/UX Design Soft Skills: Analytical Thinking, Business Communication, Stakeholder Management, Leadership, Project Management, Time Management, Documentation

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