Hi, I'm Jagvir

Hello! I'm Jagvir Dhesi, a recent graduate of Mount Royal University, where I earned a degree in Accounting and a minor in Finance. My time at university exposed me to various subjects, including information systems and statistics, which sparked my interest in the captivating field of data analytics.

After completing my degree and gaining valuable work experience, I decided to further expand my knowledge in the data field. I enrolled in an intensive Data Science program, where I delved deeper into the concepts I had previously learned in university. This program provided me with comprehensive insights into data management techniques like ETL (Extract, Transform, Load) and EDA (Exploratory Data Analysis). While also learning Python, SQL, Tableau, etc.

Through this journey, I've cultivated a passion for leveraging data to gain valuable insights and make informed decisions. I'm excited to continue exploring the fascinating world of data analytics and contributing my skills to meaningful projects.

In my free time, you can often find me immersed in anime & movies, exploring the world of video games (currently playing Baldur's Gate 3) with Skyrim being my all time fav! I'm also passionate about playing the guitar, which I'm still learning 🤓

Here are some random pictures:

KPMG

Staff Accountant / Consultant, Fall 2023 - Winter 2025

• Conducted thorough audits of financial statements, ensuring accuracy and compliance with GAAP and IFRS guidelines
• Identified and resolved discrepancies to ensure accurate financial reporting
• Leveraged Excel and Power BI to extract, quantify, and extrapolate financial insights for clients in the energy sector

TRS Staffing Solutions

Accounting Assistant, Fall 2022

• Utilized SQL Database querying to extract and analyze employee data for financial analysis and reporting, resulting in a 50% increase in efficiency
• Analyzed and synthesized large-scale data files, by leveraging advanced querying techniques and data manipulation tools, to extract valuable insights and streamline financial reporting processes
• Prepared Bank Reconciliations and Trial Balances for 3 bank accounts, handling transactions from Canada, the UK, Europe, and India, totaling over $1 million in monthly transactions

Payroll Analyst, Spring 2022

• Entered and updated information for 30+ employees/contractors, calculating pay and benefits based on attendance, leave, and overtime
• Created and streamlined onboarding profiles for new hires, with an average of over 20+ new hires per week
• Executed accurate payroll filing and remittance to the Canada Revenue Agency for accounts exceeding $1 million, ensuring compliance and accuracy
• Investigated and resolved payroll discrepancies in collaboration with recruiters, achieving a 10% reduction in payroll errors
• Consistently met deadlines for weekly payroll with efficiency and precision
• Utilized Excel proficiently, applying functions such as VLOOKUP, IF, SUM, INDEX to effectively process large datasets

FBC

Quality Assurance Technician (Internship), Winter 2021

• Accurately examined and analyzed and verified 2000+ tax documents (T4, T4A, OAS, T4E, T5, etc.)
• Surpassed performance goals by 30% of daily completion of client accounts
• Maintained regular communication with 15+ clients per week to ensure accurate recording of information
• Organized and updated client data for over 300+ clients using FBC's proprietary software

Subway

Manager, 6 Years

• Received recognition on a new monthly record of 30+ high reviews during my shifts (NPS)
• Have solved frustrations on multiple accounts regarding customer complaints and chargebacks
• Calling and emailing customers based on feedback with professional integrity
• Working with Sysco, Uber Eats, DoorDash, and Skip the Dishes to maintain proper logistics and inventory
• Reduced costs by making sure inventory levels are correct and match up with daily consumption
• Reconciling receipts and cash through POS system
• Prioritizing the safety of customers and staff during the pandemic with a high level of problem-solving (Created signage while imposing strict safety protocols)

Transforming and Analyzing E-commerce Data

SQL (PostgreSQL) & Excel

• Goal: Uncover valuable patterns and insights
• Loaded CSV files into PostgreSQL, cleaned data, and used SQL queries
• Thorough quality assurance process to ensure query accuracy
• Results: Revealed top revenue-generating countries, best-selling products, and customer behavior patterns
• Rewarding experience, honing SQL skills and gaining specialized insights into ecommerce industry
Check it out here

Statistical Modelling and APIs for Paris Bikestations

Python, Pandas, NumPy, Matplotlib, Seaborn

• Used and merged City Bikes, Foursquare and Yelp APIs
• The model explores the relationship between various independent variables, such as latitude, longitude, distance, review_count, and rating, and the dependent variable 'free_bikes,' indicating bike availability
Check it out here

Distribution of Tuberculosis

Tableau & Excel

• Identified important features of the tuberculosis dataset, including country/territory, region, year, case detection rate, and mortality of TB cases
• Discovered interesting patterns, trends, and outliers in the data, such as the concentration of TB mortalities in Africa and the correlation between detection and mortality rates
Check it out here

Clustering

Python, Pandas, NumPy, Matplotlib, Seaborn

• Two customer segmentations are created using clustering techniques. The first segmentation is based on demographics and the second segmentation focuses on banking behavior
• Factors such as savings account usage, amount saved, credit account usage, debt levels, and transaction patterns are used
• Created radar and PCA charts
Check it out here

Predicting Flight Delays

Python, SQL, Pandas, NumPy, Matplotlib, Seaborn, Sklearn, XGBoost

• Using machine learning to predict flight delays
• June, July & August have the highest average departure and arrival delays
Check it out here

Loan Predictions & Deployment

Python, SQL, Pandas, NumPy, Matplotlib, Seaborn, Sklearn, AWS

• Predicting whether a loan will be approved or rejected based on a set of input features such as gender, marital status, credit history, etc.
• The baseline logistic regression model has an accuracy score of 0.78 meaning the model was able to predict 78% of the loan applications
Check it out here

Natural Language Processing

Python, SQL, Pandas, NumPy, Matplotlib, Seaborn, Sklearn, TF-IDF

• project aims to build a machine learning model that can predict if two given questions are duplicates or not
• The logistics regression model best fits sentiment analysis tasks that involve binary classification
• Model scored 0.80 meaning the model was able to predict 80% of the questions with a precision score of 74%
Check it out here

Demand Prediction Using Time Series Analysis

Python, SQL, Pandas, NumPy, Matplotlib, Seaborn, Sklearn, TF-IDF

• The project aims to predict the sales revenue for different products across various EU countries using historical sales data
• Sales revenue is projected to increase by over 50.04%
Check it out here