Managed a $31 million portfolio by leveraging advanced data analysis techniques on ERP-derived supply chain data. This optimization of processes, resource allocation, and precise forecasting supported strategic decision-making for the newly acquired unit in Rhode Island.

Spearheaded cross-functional collaboration as the primary liaison, translating business requirements into data-driven solutions. Ensured optimized processes and resource allocation strategies, resulting in a 15% increase in operational efficiency.

Executed data extraction, transformation, and validation using SQL, producing insightful reports & dashboards with Power BI and Snowflake. Implemented stringent data validation protocols, leading to a significant 20% reduction in errors.

Applied Python-based machine learning algorithms for predictive modeling, classification, and clustering, enhancing portfolio management strategies. Achieved a notable 25% improvement in predictive accuracy through meticulous analysis and strategic application.

Rigorously validated data sources, minimizing errors and maintaining data integrity. My attention to detail ensured reliable insights.

Developed compelling reports and dashboards using Power BI and Snowflake. These visualizations empowered stakeholders to make informed decisions.

Conducted hypothesis testing, regression analysis, and A/B testing to derive actionable insights.

Created impactful visualizations using libraries like Matplotlib, Seaborn, and Plotly.

Crafted efficient queries, optimized performance, and handled large datasets.

Managed data warehouses, querying semi-structured data, and optimized query performance.

Utilized Excel for data manipulation, pivot tables, and trend analysis.

My understanding of supply chain processes allowed me to contextualize data, providing actionable insights.

Familiarity with financial processes enhanced my ability to analyze relevant data.

Proficient in interpreting procurement-related data, contributing to cost-effective decision-making 

 Created a virtual environment and established a DS folder structure using Cookiecutter to organize the project files and directories effectively.

 Set up and initialized Git for version control enabling easy tracking of changes made to the project over time.

 Conducted problem identification and exploratory data analysis (EDA) to gain insights into the data and understand the problem at hand. This involved techniques such as data visualization, statistical analysis, and data preprocessing.

 Developed a pre-processing pipeline for the data, which involved tasks such as data cleaning, handling missing values, feature engineering, and data transformation for training supervised and unsupervised machine learning algorithms (regression, decision trees/random forest, neural networks, feature selection/reduction, clustering, parameter tuning, etc.

 Utilized GitLab for local commits and pushed to server repositories keeping track of different versions.

 Tracked experiments using ML Flow and trained the model on both CPU and GPU.

 Packaged the model using BentoML and tested the API using Swagger UI, Postman and Python code for easy integration of the model into other applications and systems.

 Implemented ETL pipeline design and constructed a Shapash dashboard to display global and local level interactive visualizations and insights into the model's predictions.

 Deployed a Dockerized web app and model on an AWS EC2 instance.

 Assisted graduate admission team with the admission process and other academic requirements.

 Assisted and displayed exceptional problem-solving ability in recognizing problems, validate business and system processes, collect, and testing data, establish facts, and draw solid conclusions using information technology.

 Utilized data structures to improve workflows by using the skill of SQL on student’s database and performed data manipulation data conversion, statistical methodologies, and integrated information systems.

 Managed the admissions process by using the Slate platform, overseeing the review of thousands of applications every intake.

 Developed and implemented new procedures by introducing a data pipeline, employed data integrity principles and suggested data storage strategies for usability and supported in data sourcing, data modelling, data integration and data access methods for handling international student applications, resulting in a 25% decrease in processing time for each application.

 Trained and supervised new graduate assistants, ensuring that all application materials were reviewed accurately and efficiently.

 Interacted with entire campus community (faculty, staff, and students) and assisted with various technological inquiries, with a heavy emphasis on customer service, risk management and root-cause analysis to support admissions department.

 Demonstrated strong attention to detail, time management, and communication skills throughout the admissions process.

 Translated client requirements to design solutions across multiple projects involving end-to-end design of aero engine parts.

 Provided Machine Learning algorithms and Data Mining solutions to various business problems.

 Developed QNS tool which has a collection of more than a million records to easily track and make decisions using Machine Learning techniques. This Saved 102,000 USD and 1650 hrs annually by automating the working process.

 Used Big Data techniques and predictive analysis to identify frequently occurring defects.

 Used Data Visualization technique to create dashboards for analyzed data.

 Managed 6-member team, organized quality and 6 sigma audits frequently for delivering high quality solutions.

 Hired and onboarded new associates, providing guidance and knowledge transfer to ensure a smooth transition into the team.

 Served as a Diversity and Inclusion Ambassador, promoting a culture of inclusivity and equity in the workplace.

 Demonstrated strong project management, communication, and leadership skills throughout the event planning process by organizing team building activities.