Experience

Innomatics Research Labs

• India
• E-Learning Providers
• 1 - 100 Employee

• Data Science Intern

  • Jul 2021 - Oct 2022

  Gained hands-on experience in industry best practices and modern data science standards under the guidance of a senior data scientist mentor. Developed and presented data-driven projects encompassing exploratory data analysis, data cleaning, machine learning, statistical modeling, study design, and proficiency in using SQL, Excel, and Tableau for data manipulation and visualization. Leveraged statistical analysis and probability concepts to draw meaningful insights and support informed decision-making processes. Show less



U.S. Bank

• United States
• Legal Services
• 1 - 100 Employee

• Application Developer

  • Sep 2018 - Mar 2021

  Key Deliverables: * Actively responsible for spectrum of tasks like – interfacing with development team to implement Push Notification Application for mobile users of US Bank, reviewing code/assessing the impact of submitted changes, documentation of technical designs after design review sessions and so on * Played a key role working under Agile process, kept Scrum meetings for team members; writing queries, optimizing SQL statements for various projects to ensure it gives the desired output * Utilized Excel extensively for data analysis, employing advanced functions such as pivots, lookups, and other data manipulation techniques to derive actionable insights and support data-driven decision-making processes. Show less



Thinkful

• United States
• E-Learning Providers
• 300 - 400 Employee

• Data Scientist

  • Apr 2020 - Jan 2021

  • Learned industry best practices and modern data science standards alongside a senior data scientist mentor • Developed and presented projects involving exploratory data analysis, data cleaning, machine learning, statistical modeling, study design, statistics and probability Notable Projects Voice Based Gender Detection • The project is aimed at analyzing the effectiveness and performance of different machine learning Algorithms in its ability to identify voice as male or female, based upon on the acoustic properties • Techniques used: Data Cleaning, Feature engineering, PCA, Logistic regression, Support Vector Machines, KNN Classifier, random forest modeling, XGBOOST • Built with: Python, Pandas, Scikit-Learn, Matplotlib , Seaborn Segmenting Credit Card Customers with Machine Learning • The aim of this project requires to develop a customer segmentation to define marketing strategy based on the usage behavior of about 9000 active credit card holders with 18 behavior variables during the last 6 months. • Techniques used: Data Cleaning, Feature engineering, PCA, t-SNE, UMAP, K-means, Hierarchical, DBSCAN, Gaussian Mixture Clustering models • Built with: Python, Pandas, Scikit-Learn, Matplotlib, Seaborn Show less