Data Analyst Power Bi

KSHITIZ BADOLA

New York, USA +1-201-***-**** *******.***@******.*** LinkedIn GitHub Google Scholar SUMMARY

Data Analyst with about 4 years of experience in financial and eCommerce sectors, skilled in SQL, Python (Pandas, Numpy), and data visualization tools like Power BI and Tableau. Proven track record in fraud detection, financial forecasting, and customer behaviour analysis, driving efficiency, improving risk management, and enhancing business strategies. Expertise in AWS S3 data migration and predictive modeling.

TECHNICAL SKILLS

Methodologies: SDLC, Agile/ Scrum, Waterfall

Language & Databases: Python, SQL, R, MySQL, MS SQL Server, ETL. Python Packages: Pandas, NumPy, Matplotlib, SciPy, Scikit-Learn, SeaBorn, PyTorch, ggplot2, Plotly Data Analytics Skills: Data Manipulation, Data Cleaning, Data Visualization, Exploratory Data Analysis, Data Analysis Others Tools: AWS, AZURE(Databricks), NLP, A/B Testing, Hypothesis testing, ETL, Hadoop, Spark, Big Query, Apache Airflow, Tableau, Power BI, QlikSense, QlikView, Advanced Excel, Visual Studio, Jupyter Notebook, Jira, Confluence Visualization: Tableau, Google Data Studio, Power BI Collaboration Tools: Jira, Confluence, Agile

Version Control: Git, GitHub

PROFESSIONAL EXPERIENCE

J.P. Morgan Chase & Co. New York, USA

Data Analyst Dec 2023 - Present

Validated static and dynamic financial data using SQL triggers and stored procedures for 400+ data points, cutting data extraction time by 96 minutes per batch.

Reviewed 500+ SQL queries with UNION clauses, maintaining a 2.5M-record fraud-claim database, preventing an

$85M loss through early fraud detection.

Analyzed 33M financial transactions using Pandas and Numpy, improving risk management by 12% through pattern recognition in time-series data.

Utilized Python SciPy & PyTorch for data cleaning and pattern analysis, boosting financial forecasting accuracy by 15%.

Integrated Power BI dashboards, enhancing real-time analysis and cross-department collaboration by 20%.

Led HIPAA-compliant migration of 8+ TB of Chase Bank data to AWS S3, achieving 99.999% data durability and reducing recovery time by 60% with cross-region replication. Environment: MS SQL Server, Python (Pandas, Numpy, SciPy, PyTorch), Jupyter Notebook, Power BI, AWS S3, SQL Triggers, Stored Procedures, UNION Clauses.

HealthKart Bengaluru, India

Data Analyst Apr 2019 - Aug 2022

Analyzed 1 million+ customer transactions and product interactions from eCommerce, CRM systems, and Google Analytics, identifying key trends in customer preferences that boosted sales conversion by 15% and customer retention by 12%.

Utilized SQL, Python (Pandas, NumPy), and Tableau to extract, process, and visualize data, creating interactive dashboards that enabled real-time decision-making across marketing, sales, and product development teams.

Conducted statistical analysis (regression, hypothesis testing) and built predictive models (logistic regression, decision trees) to forecast customer churn, improving retention by 18% through data-driven marketing strategies.

Led A/B testing and performance analysis on user engagement metrics (e.g., bounce rate, cart abandonment), resulting in a 7% improvement in overall platform engagement and 10% increase in average order value (AOV).

Collaborated with a team of 5 to integrate external healthcare product datasets for inventory optimization and dynamic pricing, reducing stockouts by 5% and aligning data insights with strategic business objectives. Environment: SQL, Python (Pandas, NumPy, Scikit-learn), Tableau, MySQL, PostgreSQL, Google Analytics, Excel, and A/B Testing tools.

EDUCATION

Master of Science in Data Science Dec 2023

New York Institute of Technology NY, USA

Bachelor of Technology in Computer Science Engineering Jun 2022 Guru Gobind Singh Indraprastha University Delhi, India PROJECT

Predicting Fraud in Bank Payment Services USA (Link)

Utilized SPSS for advanced statistical modeling and integrated Snowflake for scalable data storage, led to reduction in fraud detection processing time.

Applied metadata documentation & ad-hoc analysis using Tableau and Power BI, streamlining data organization & improving fraud prediction efficiency by 33%.

Integrated Looker with payment service datasets to visualize fraudulent transaction patterns, driving an improvement in fraud detection efficiency.

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