Data · Business · Operations Analyst

Turning data into
decisions that move
the business.

London, ON, Canada · SQL · Python · Power BI · Tableau

Analytical and detail-oriented professional who transforms operational, customer, and financial data into insights that improve efficiency, reporting accuracy, and business outcomes.

Heta Chavda, Data Analyst
Heta ChavdaData Analyst · London, ON
Portfolio SnapshotLIVE
13
GitHub Projects
99%
Processing Accuracy
40%
Reporting Effort ↓
15%
Efficiency ↑
About Me

Analytics across operations, customer, and finance

I'm a data analytics professional with a Master of Data Analytics and hands-on experience turning messy, real-world data into clear recommendations. My work spans logistics operations at Amazon, customer analytics for a service business, and financial reporting for a banking client.

I'm proficient across the full stack — SQL and Python for analysis, Power BI and Tableau for dashboards, and machine learning for prediction — with a focus on KPI reporting, trend analysis, and process improvement that leaders can act on.

LocationLondon, ON 🇨🇦
EducationM.Sc. Data Analytics
FocusBI · ML · Reporting
StatusOpen to opportunities
Technical Toolkit

Skills & Technologies

The tools I use to collect, model, visualize, and communicate data.

SQL92%
Power BI95%
Python88%
Tableau85%
Excel96%
Machine Learning82%

Data Analytics

SQLPythonRStatistical AnalysisPredictive AnalyticsMachine LearningData Modeling

BI & Visualization

Power BITableauLooker StudioKPI ReportingInteractive Dashboards

Databases

PostgreSQLMySQLPresto SQLDatabase DesignQuery Optimization

Python Libraries

PandasNumPySciPyScikit-learnMatplotlibSeabornTensorFlow

Data Management

ETL ProcessesData CleansingTransformationIntegrationGovernance

Tools & Collection

Excel (Pivot · Macros)Git / GitHubJIRA · AgileSeleniumGA4 · GTM
Career & Education

Experience Timeline

Roles across logistics operations, customer analytics, and financial reporting — plus my academic foundation.

2025 — PRESENT
Operations Associate
Amazon Delivery Station — London, ON
  • Exceeded productivity targets by 10–15% through workflow-efficiency analysis and process optimization.
  • Maintained 99% processing accuracy while handling 1,500+ packages/shift.
  • Reduced dispatch delays by 10% by resolving routing and staging exceptions.
2025
Data Analyst Intern
Sense of Beauty SPA & Brow Bar — Niagara Falls, ON
  • Cut reporting effort by 40% with automated Power BI dashboards for bookings, revenue & operations.
  • Improved scheduling efficiency by 15% via demand & marketing-performance tracking.
  • Enhanced customer-journey visibility through GA4 and Google Tag Manager.
2022 — 2023
Associate Data Analyst · Client: CCIL / RBI
Munimshree Accounting & Tax Services — Dakor, India
  • Improved data integrity through enhanced validation and reconciliation controls.
  • Built KPI dashboards for clearing & settlement operations and financial performance.
EDUCATION
Master of Data Analytics
University of Niagara Falls — Canada
EDUCATION
Master of Commerce · Advanced Accounting
Sardar Patel University — India
Portfolio · 13 Projects

Featured Projects

Each is a full case study — business problem, methodology, insights, and impact — on GitHub.

Visualization Gallery

Dashboards & Visualizations

Real dashboards built from project results — model performance and customer segmentation.

bank-loan-default.dashboard
Bank Loan Default — Model Performance Logistic Regression · 37,138 loan applications · Python / scikit-learn ROC-AUC SCORE0.915▲ strong LOANS ANALYZED37,138 DEFAULTS CAUGHT62%768 / 1,245 ROC Curve — Discriminative Power random Area Under CurveAUC = 0.915 Confusion Matrix — Test Set (7,428) PREDICTEDRepaidDefault 3,922True Repaid ✓ 2,261Missed default 477False alarm 768True Default ✓ What Drives Default? — Feature InfluenceBars right = raises default risk · left = protective Interest Rate+0.95 Debt-to-Income+0.55 Employment Length+0.40 Annual Income−0.15 Loan Outcome Distribution 37,138total loans Fully Paid83% Charged Off14% Current3%
customer-segmentation.dashboard
Customer Segmentation DashboardK-Means clustering · 100 customers · 4 marketing segments CUSTOMERS100 SEGMENTS FOUND4 HIGH-PRIORITY2 Segments by Age & Income Age →Income → Young ProsWealthy SeniorsMod. EngagedRetirees Marketing Priority by Segment Young ProfessionalsHIGH Wealthy SeniorsHIGH Moderately Engaged ProsMEDIUM Low-Income RetireesNURTURE

▸ Interactive Power BI & Tableau dashboards available in each project repository on GitHub.

Credentials

Certifications

Verified course completions in analytics, visualization, and cloud.

Dashboards vs Data Stories certificate
Dashboards vs. Data Stories
LinkedIn Learning
Python for Non-Programmers certificate
Python for Non-Programmers
LinkedIn Learning
Career Skills in Data Analytics certificate
Career Skills in Data Analytics
LinkedIn Learning
Google Cloud Core Infrastructure certificate
Google Cloud: Core Infrastructure
Google Cloud
Introduction to JIRA certificate
Introduction to JIRA
Simplilearn SkillUP
+ Databricks & CSCMP
Additional credentials
🧱
Databricks Fundamentals
Databricks
☁️
Google Cloud: Core Infrastructure
Google Cloud
📊
Data Visualization: Storytelling
LinkedIn Learning
🚚
Supply Chain & Operations
CSCMP
🎓
Educational Credential Assessment
World Education Services (WES)
📈
Master of Data Analytics
University of Niagara Falls
Get in touch

Let's work together

Open to Data Analyst, Business Analyst, and Operations Analyst opportunities.

Phone
(416) 856-3451
Location
London, Ontario, Canada
Bank Loan model shippedROC-AUC 0.915 · 13 projects live