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About

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Mohit Appari

Quantitative Finance | Data Science
APD, Florida
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I enjoy conversations around data, math, and statistics—particularly when they connect to real-world situations. There’s something interesting about exploring patterns, asking questions, and gradually making sense of complex data. Looking for exciting opportunities to work and collaborate with bright minds and explore the world of data together.

If you're embarking on an exciting project or seeking fresh perspectives, don't hesitate to reach out via Mail or Whatsapp.  I'm always eager to connect, exchange ideas, and explore new avenues of exploration and growth together.

Check out some of my photography work on VSCO.



About this site

Welcome to my home on the internet. This site functions as a blog/portfolio, a place to share code and thoughts. Opinions of my own.

I learnt how to build this site from the most awesome people in the community:

Experience

Data Analyst  @  APD
June 2024 – Present
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Authored and maintained 40+ automated reporting pipelines across daily, weekly, monthly, quarterly, and annual cadences — built in Python, R, SQL, and SPSS, processing client, service, provider, and waiver enrollment data from Azure Data Warehouse and SQL Server for distribution to 50+ staff and senior leadership.
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Managed analytics and built forecasting models for ~$190–200M in annual program payments and budget allocations across 60,000+ clients, producing compliance tracking reports and fiscal planning outputs to support statutory deadline adherence and regulatory reporting.
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Optimized SQL data extraction pipelines via query partitioning, datetime filtering, and cache clearing, reducing data pull time from ~1 hour to under 10 minutes (83% faster), directly accelerating reporting turnaround across all cadences.
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Research Assistant  @  Florida State University
June 2024 – September 2024
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Researched generative models (GANs, VAEs, RNNs, Diffusion) under Dr. Bin Ouyang for synthetic molecular data generation, focused on discovering stable, sustainable materials for lithium-ion battery applications using AI-driven compound synthesis.
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Implemented and benchmarked NequIP (MIT open-source neural equivariant potential) on an 11,000+ material dataset, then applied the model to a custom dataset.
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Collaborated with PhD researchers to align data science workflows with experimental research goals, contributing to interdisciplinary work on AI-driven material discovery and energy storage.
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Software Developer  @  S&P Global
January 2022 – August 2023
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Independently researched, wrote, tested, and deployed a full migration of legacy .NET data extraction pipelines to Python, owning the entire lifecycle from proof-of-concept to production for a team of 8 across the Capture pipeline.
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Engineered automated document extraction pipelines using OCR, image recognition, regex, and Grooper to process 10,000+ daily files (PDFs, TIFs, images) across oil & gas, energy, manufacturing, and automotive sectors, formatting raw documents into structured, analytics-ready outputs.
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Deployed production pipelines to live servers with automated file-drop triggers, integrated final outputs into Power BI for downstream visualization, and maintained delivery cadence through daily DevOps board management and cross-team sprint deadlines.
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Software Developer Intern  @  LG Electronics
May 2021 – August 2021
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Built a proof-of-concept LG WebOS logistics platform in a 2-week ideathon, integrating real-time shipment tracking, live delay and congestion feeds, and a computer vision drowsiness detection system (Python, OpenCV, YOLO) achieving 90% facial landmark accuracy for driver safety.
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Designed and integrated a CNN-based object recognition module with REST APIs and PostgreSQL for automated package sorting, reducing sorting time by 25% across the logistics workflow.
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Awarded 3rd place out of all competing teams. Presented the solution to LG leadership and engineering teams, receiving commendation for practical application and technical innovation.
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Software Developer Intern  @  ITC Limited
January 2021 – March 2021
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Built a Master Data Management system using the MERN stack with role-based authentication (admins, users, vendors) to manage commodity tracking and application workflows across multiple ITC business units.
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Implemented approval workflows and role-based access controls to enhance data security and streamline user operations across business units.
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Automated product tracking and data updates, reducing manual workload and improving cross-unit data accuracy through API integrations and backend synchronization.
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