About me

I'm a USC Master's Grad Student. I love to build advanced ML algorithms to solve impactful business problems. I’m overall a person who likes to take up challenges which are intellectually stimulating and over the past few years I have honed my skills through various projects.

I am passionate about using data-driven insights to drive positive change and solve real-world challenges. If you are looking for a Data Scientist/Machine Learning Engineer with a track record of delivering innovative solutions, I would love to connect and discuss how I can contribute to your organization.

What i'm doing

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    Data Analytics

    Unveiling insights through the artful interpretation of data, crafting actionable intelligence from information's intricate patterns.

  • Pred Model icon

    Predictive Modeling

    Decoding the future with smart algorithms, turning patterns into predictions for informed decision-making.

  • stats icon

    Statistics

    Unveiling truths through rigorous hypothesis testing, translating complex data into actionable insights with precision.

  • data science icon

    Data Science

    Crafting actionable intelligence from data insights, decoding the future with smart algorithms, and unveiling truths through rigorous statistics.

Daniel lewis
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Daniel lewis

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Resume (Download PDF)

Education

  1. University of Southern California

    Aug 2021 — May 2023

    M.Sc., Machine Learning and Data Science GPA 3.81/4.0
    Relevant Courses: Machine Learning, Deep Learning, Databases, Applied and Cloud Computing, Data Structure and Algorithms, Linear Algebra, Probability Theory, Digital Signal Processing

  2. Vellore Institute of Technology

    July 2017 — July 2021

    B.Tech., Electronics and Communication Engineering GPA 8.95/10.0
    Relevant Courses: Machine Learning, Data Mining and Predictive Analysis, Computer Vision, Big Data Analytics

Experience

  1. Data Scientist @ Prime Healthcare

    Feb 2024 — Present

    ◦ Leveraged Prophet (Meta Open Source) to analyze supply utilization and purchasing trends, optimizing reorder points and order quantities. Reduced stockouts by 30%, saving $1.2 million annually in expired inventory and capital.
    ◦ Developed a RAG model with Llama 3.1 to web scrape and categorize healthcare items, suggesting substitutes to enhance purchasing decisions. Improved item substitution accuracy by 25%, streamlining procurement and reducing manual categorization efforts.

  2. Machine Learning Intern @ CarmaCam

    Aug 2023 — Feb 2024

    Devising two approaches to identify and classify road signs for autonomous vehicles:
    (1) AutoML on Google Cloud platform, and
    (2) transfer learning with various architectures (ResNet50, Xception, and InceptionResNetV2).

  3. Data Scientist @ USC ITS - Office of CISO

    Feb 2022 — May 2023

    ◦ Redesigned the risk prediction framework, achieving improved F1-score of 0.91 for 28,000 vendors of USC.
    ◦ Implemented XGBoost model, accomplished 15% reduction of false positives, through rigorous A/B testing.
    ◦ Automated processes for alerting vendors of their risk ratings on Power BI, provided data analysis findings to stakeholders with recommendations to mitigate vendor risks. Cut down 20+ hours of weekly manual work.

  4. Data Science Research Intern @ Vellore Institute of Technology

    Nov 2020 — July 2021

    ◦ Implemented novel efficient deep-learning model to diagnose patients with COVID-19 or pneumonia from X-ray images.
    ◦ Employed Unet encoder-decoder models, improved training speeds by a factor of 2, achieving low FLOPs comparable to state-of-the art models.
    ◦ Deployed this network achieving 99.3% accuracy and 99.31% F1-score in Micronet M3 model.

  5. Machine Learning Intern @ Arista Networks

    Nov 2020 — July 2021

    ◦ Received theoretical as well as hands-on training on concepts of fingerprinting along with ML algorithms in 1 week.
    ◦ Leveraged k-Nearest Neighbor and Random Forest models to estimate user position in an indoor environment. Using Wi-Fi and inertial sensors yielded positioning as precise as 2-3 m.
    ◦ Designed algorithm to apply concepts of RSSI to extract real-time location of client devices operating on access points of WiFi routers placed across work facility with an accuracy of 0.98.

My skills

  • ML Modeling
    90%
  • Data Analysis
    80%
  • Coding Languages (Python, SQL)
    90%
  • Python Libraries - scikit-learn, PyTorch, TensorFlow, Pandas, NumPy, Matplotlib, OpenCV
    90%
  • Tools (AWS, GCP, PowerBI, Anaconda, GitHub)
    80%

Contact

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