Kanishka Fartiyal
Data Scientist
github.com/KanishkaFartiyal | candidates.intervues.club/u/kanishka-fartiyal
SUMMARY
Data Scientist and Data Analyst with experience in data cleaning, exploratory data analysis, and building business decision dashboards. Skilled in developing FastAPI endpoints, applying statistical methods, and analysing performance metrics across large datasets. Proficient in Python, SQL, data visualisation, and statistical libraries to deliver actionable insights.
EXPERIENCE
Data Analyst Intern
Ingrify (Verinova Labs)
Jun 2026 – Aug 2026
- Collected, validated, and cleaned 499 real-world product records, standardising messy source data into a decision-ready dashboard and reducing manual lookup time by approximately 40%.
- Engineered 12+ FastAPI, SQLAlchemy, and MySQL endpoints to execute data validation checks and power search, filtering, and business reporting.
- Analysed attributes across 6 classifications and 30+ brand categories, converting validated data into visual comparisons for competitive benchmarking and executive reporting.
Content Analyst
Content Analytics (Freelance)
Oct 2025 – Present
- Tracked view, engagement, and retention metrics in Excel to evaluate performance trends and establish repeatable reporting templates.
- Conducted a performance test comparing content formats, identifying that hook delivery under 3 seconds with pattern interrupts increased average watch-time by 2x, and documented findings into client briefs.
EDUCATION
CMR University · Bachelor of Computer Applications (BCA) · 2024 – 2027
KVM Public School · 12th Grade (Senior Secondary) · 2024
SKILLS
Languages and Frameworks: JavaScript, Jupyter Notebook, Python, SQL, FastAPI, Matplotlib, NumPy, Pandas, Scikit-learn, Seaborn, SQLAlchemy
Databases: MySQL, SQL Server
Tools: Excel, Git, Google Cloud Platform, Power BI
PROJECTS
E-Commerce Sales & Customer Segmentation
Cleaned and analysed 96,477 real orders from the Olist dataset across 9 product categories and 3 years of transaction data to uncover revenue and behaviour trends. Applied statistical analysis using Python, Pandas, and Scikit-learn. Cleaned and analysed 96,477 orders across 9 product categories over 3 years of transaction data to identify customer behaviour and revenue trends. Applied statistical models and machine learning techniques using Python, Pandas, and Scikit-learn.
Python, Pandas, Scikit-learn
Tech Industry Layoffs & Data Science Salary Analysis
Cleaned and standardised 746,809 records across 1,703 companies (2020–2025) using advanced SQL (CTEs, window functions, CASE WHEN) to answer 8 business questions on trends and geography. Standardised 746,809 records spanning 1,703 companies utilizing CTEs, window functions, and conditional logic in SQL Server. Visualised geographic and economic industry trends using Python, Pandas, and Seaborn.
Python, SQL Server, Pandas, Seaborn
AWARDS
Excel Dashboards for Business Analytics, Data Analysis using Excel, CS50P – Introduction to Programming with Python, Data Analysis – Beginner to Advanced