Machine Learning Business Intelligence

Shubham Dinesh Parulekar

+1-217-***-**** ****@********.*** LinkedIn Champaign, IL

EDUCATION

University of Illinois Urbana-Champaign, Champaign, IL August 2024 - May 2026 MS in Information Management GPA 3.9/4.0

Coursework: Data Science, Data Mining, Data Warehousing and Business Intelligence, Statistics and Data Modelling Sardar Patel Institute of Technology, Mumbai, India June 2017 - May 2021 Bachelor of Technology in Electronics and Telecommunications GPA: 8.13/10 Coursework: Data Structures and Algorithms, Database Management, Machine Learning and Artificial Intelligence, Cryptography SKILLS

Programming Languages: Python, R, SQL, NoSQL, C, C++, HTML, CSS, JavaScript Tools and Frameworks: Microsoft PowerBI, Tableau, Microsoft Office Suite, Git, Jira, Apache Spark, Airflow. Libraries: Pandas, NumPy, Matplotlib, Seaborn, Plotly, openCV, scikit-learn, Tensorflow, Pytorch. Cloud Services: AWS: Sagemaker, Lambda, Redshift Azure: Data Factory, Databricks, SQL Server GCP: GKE, CDN, Cloud Storage Certification: Microsoft Certified Data Analyst, Professional Scrum Master, Associate in General Insurance (AINS) Soft Skills: Communication Skills, Project Management, Attention to detail, Critical thinking, Problem-solving, Visualization and Storytelling.

WORK EXPERIENCE

Business Analyst Quantiphi Analytics July 2021 - August 2024

● Collaborated on the end-to-end development of an AI-powered Intelligent Document Processing solution, reducing processing time by 80% by automating data extraction and validation

● Drove client engagement, performed pre-sales market research, and presented sales materials, including presentations, white papers, and case studies for over 50+ presentations and 8 tailored deployments, leading to a 15% increase in pitch success

● Designed industry-specific use cases and product features that met 100% of user requirements and enhance efficiency, usability, compliance, and user engagement

● Led the proposal drafting and strategy planning for 50+ client demos, securing $3M+ in contracts with a 30% conversion rate

● Partnered with cross-functional teams to develop and integrate machine learning and large language models into business intelligence solutions, enhancing predictive capabilities Business Analyst Intern Quantiphi Analytics January 2021 - July 2021

● Built interactive data visualizations using Power BI and MicroStrategy to present key market metrics and product metrics to non-technical stakeholders, accelerating data-driven decision-making by 20%

● Conducted extensive market research through 20+ discovery sessions with industry leaders, generating insights that shaped product roadmap and strategy planning

● Worked with cross-functional teams ensuring the solution met user needs and business objectives and executing a solid business plan that achieved 100% on-time delivery of solutions.

● Improved data validation and reporting processes by crafting effective SQL queries and streamlining data pipelines, decreasing reporting time by 30% and enhancing data accuracy for performance monitoring. Data Analyst Intern BitGenie Technologies April 2020 - July 2020

● Developed a responsive website and interactive dashboards using Power BI and Tableau, providing real-time access to key KPIs and boosting data retrieval by 30% and stakeholder engagement by 25%.

● Automated data workflows, conducted data mining, data analysis, and performance tracking using Python, cutting data processing time by 60% and enabling a 30% gain in efficiency. ACADEMIC AND PERSONAL PROJECTS

Detection and Classification of Breast Cancer Cells from Mammogram Images April 2021

● Implemented a neural network-based model predicting the early onset of breast cancer using mammogram images with more than 95% accuracy, enhancing diagnostics capabilities

● Built a secure, responsive user-facing app using Flask-AWS delivering reports in less than 10s, boosting early detection by 40%, and cutting manual processing

● Authored and published a paper on the same in the academic journal ‘IJRASET’ Volume 11, Issue X, October 2023 Real-time Vehicle Detection for Autonomous Driving November 2020

● Developed a computer vision based model for real-time detection of vehicles, pedestrians, traffic signals, signs, etc with more than 90% accuracy and less than 1s latency

● Integrated the model into a scalable pipeline using OpenCV and Kubernetes for real-time video processing of 60+ FPS and enhancing autonomous driving reliability by 35%

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