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About

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Mohit Appari

Data Scientist
APD, Florida
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I’m someone who’s deeply passionate about data and everything it powers. My current focus lies at the intersection of data science and finance. I’m always eager to work on impactful problems, collaborate with sharp minds, and push the boundaries of what’s possible with 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.



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 Scientist  @  APD
June 2025 – Present
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Conducted in-depth statistical analysis using SPSS, SAS, and R on large-scale datasets related to individuals with disabilities, identifying regional trends, billing anomalies, and compliance issues that informed regulatory reporting and strategic decision-making.
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Developed interactive dashboards in Tableau, Power BI, and Excel to visualize KPIs, forecast trends, and track operational performance, streamlining executive reporting and supporting agency-wide transparency.
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Worked within the Data and Quality Unit to ensure data accuracy and reportability; created statistical summary tables and insights that highlighted discrepancies in claims and expenses, improving data reliability and enhancing forecasting precision across APD financial workflows.
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AI & Data Researcher  @  Florida State University
June 2024 – September 2024
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Researched open-source generative AI models, algorithms (GANs, VAEs, RNNs) to explore synthetic data generation for material property prediction and battery optimization applications.
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Implemented and benchmarked NequIP (MIT Open Source) for molecular stability prediction using a dataset of 11,000+ materials; improved prediction consistency by∼15% during initial validation. View more at https://bin-ouyang.com/people/
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Collaborated with PhD researchers in Chemistry to align data science workflows with experimental research goals, enhancing interdisciplinary research on AI-driven material discovery and energy storage.
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Data Scientist  @  S&P Global
January 2022 – June 2023
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Engineered scalable Python pipelines using pdfplumber, Camelot, Tesseract, and Amazon Textract to extract structured data from 10,000+ complex financial and automotive documents, reducing manual processing time and costs by over 90%.
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Automated web data extraction workflows by scripting robust scraping and parsing logic in Python, deploying solutions to internal servers with scheduled execution, and implementing custom error handling to ensure reliable data ingestion across financial sources.
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Collaborated with cross-functional stakeholders to optimize data extraction logic and implement NLP-based entity recognition, resulting in improved reporting accuracy and reduced document processing turnaround times.
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SDE Intern  @  LG
May 2021 – August 2021
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Participated in a 2-month LG internship combining webOS training and a hackathon, developing a smart logistics and safety solution.
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Built a real-time drowsiness detection system using Python, OpenCV, YOLO, and Raspberry Pi, achieving 90% facial landmark accuracy to enhance driver safety. Designed a logistics tracking system using CNNs and REST APIs with PostgreSQL, reducing sorting time by 25% and securing 3rd place in LG’s Ideathon.
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Presented the project to LG leadership and engineering teams, receiving commendation for practical application and technical innovation.
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SDE Intern  @  ITC
January 2021 – March 2021
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Developed a master data management system using Python (Flask) and MySQL, enabling multi-role access for vendors, users, managers, and administrators as an intern. Implemented role-based authentication and approval workflows to enhance data security and streamline user operations.
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Automated product tracking and data updates, reducing manual workload by 30% and improving data accuracy across business units.
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Collaborated with backend teams to integrate APIs and support cross-platform data synchronization for operational efficiency.
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