Data Analyst Power Bi

Ankush Dilip Tonde

******@**********.*** • 571-***-**** • LinkedIn • GitHub

PROFESSIONAL SUMMARY

Data Analyst with over 4 years of experience uncovering business opportunities through advanced analytics, statistical modeling, and intuitive data visualization. Proficient in transforming complex, large-scale datasets into meaningful insights using Python

(NumPy, Pandas), SQL, Tableau, Power BI, and Snowflake. Adept at designing A/B tests, ANOVA, and hypothesis-driven experiments to validate strategic initiatives, and delivering cloud-enabled analytics solutions on AWS and Azure. SKILLS

Languages & IDEs: Python, R Programming, SQL, NoSQL, Visual Studio Code, Jupyter Notebook Statistical & Machine Learning Techniques: Descriptive & Inferential Statistics, Regression Analysis, A/B Testing, Statistical Modeling, Cohort Analysis, Hypothesis Testing, Regression Models, Decision Trees, Random Forests, Naive Bayes, SVM Data Processing & Management: ETL, Exploratory Data Analysis (EDA), Data Cleaning, Data Transformation, Feature Engineering, Data Modeling, Data Integration, Data Warehousing Packages & Frameworks: NumPy, Pandas, Matplotlib, SciPy, seaborn, Scikit-learn, TensorFlow, Keras Data Visualization & Business Intelligence: Tableau, Power BI, Excel (Advanced), Looker, DAX, VBA, KPI Dashboard Design, Data Storytelling, D3.js, Gephi, Google Analytics

Data Governance: GDPR, PII Masking, Data Quality Audits, Metadata Management Cloud & Database Platforms: AWS (S3, Lambda), Azure (Data Lake, Databricks), GCP (BigQuery), MySQL, SQL Server, Oracle, MongoDB, Snowflake, SQL Server Management Studio

EDUCATION

Master's in Data Analytics Engineering May 2025

George Mason University, Fairfax, VA

WORK EXPERIENCE

Jackson Financial, USA Data Analyst January 2025 – Present

Enhanced insurance claims fraud detection accuracy by 18% by engineering regression and decision tree models incorporating insurance-specific factors such as policyholder history, claim timing anomalies, and regional risk indicators, enabling faster identification of suspicious activity.

Streamlined policy, claims, and customer service data processing by automating ETL workflows in Python (Pandas, NumPy) for over 15 million records, reducing actuarial and underwriting data preparation time by 30%.

Built interactive Tableau and Google Analytics dashboards to monitor policy conversion rates, average claim approval times, and customer satisfaction scores, driving a 12% growth in targeted cross-sell and policy upgrade campaigns.

Conducted A/B testing and hypothesis-driven statistical analysis of marketing and retention initiatives, uncovering patterns that decreased policy lapse rates by 9.7% over two quarters.

Strengthened enterprise data governance by instituting data quality checks, defining data lineage for key insurance datasets, and collaborating with compliance teams to maintain adherence to NAIC and state-level regulatory requirements.

Queried and analyzed structured insurance data from BigQuery and Snowflake to produce monthly risk, frequency, and premium trend reports, empowering actuarial teams with accurate forecasts for loss ratio management and pricing adjustments. KPIT Solutions, India Data Analyst February 2020– July 2023

Performed A/B testing and hypothesis-driven analytics on connected vehicle telemetry to assess firmware update impacts on fuel efficiency, delivering a 6% mileage improvement in hybrid vehicle fleets.

Developed cohort-based degradation models in Python and MySQL to track component wear, enabling predictive maintenance for EV battery systems and reducing unscheduled service incidents by 15%.

Consolidated IoT sensor streams, manufacturing execution data, and customer feedback into Azure Data Lake & Databricks, creating a unified mobility analytics repository for engineering and service teams.

Designed Power BI dashboards (DAX, Excel) tracking part failure rates, vehicle uptime, and diagnostic trouble codes, empowering engineers with real-time diagnostics and actionable insights.

Applied statistical modeling and hypothesis testing on EV adoption trends using ArcGIS geospatial analytics, guiding regional market expansion and charging infrastructure planning.

Crafted and automated data pipelines & warehousing solutions for large-scale automotive datasets, ensuring scalable and efficient reporting for global mobility programs.

Engineered interactive D3.js visualizations mapping live vehicle performance metrics and sensor fault heatmaps, accelerating root cause identification in R&D investigations.

Visualized driver behavior patterns and sensor anomaly clusters with Seaborn and SciPy, contributing to ADAS feature optimization and improved road safety performance. CERTIFICATIONS

Google Data Analytics Professional Certificate – Coursera (In Progress) Covered: Data Wrangling, Data Visualization (Tableau), Data-Driven Decision Making, SQL, Case-Based Projects

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