About me

I build practical ML systems that move business metrics. My work spans model serving and inference pipelines, RAG workflows, and automation tooling that saves time for operators.

I care about measurable outcomes, clear communication, and shipping data products that teams actually use. If you are hiring for data science or ML engineering, I would love to connect.

Current focus

ML systems, model serving and inferencing, and infra pipelines

Designing scalable pipelines for internal operations, improving automation, and deploying models for reliable serving.

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4+

Years in ML and data

10+

Projects shipped

$3.5M

Savings delivered

ML Systems Infra Pipelines ETL Pipelines Model Serving Automation ML Software Engineering

What I'm doing

  • analytics icon

    Data Analytics

    Turning messy operational data into reliable dashboards and decision-ready metrics.

  • Pred Model icon

    Predictive Modeling

    Forecasting demand and risk with models that are tested, monitored, and explainable.

  • stats icon

    Statistics

    Designing experiments and statistical tests to back decisions with evidence.

  • data science icon

    Data Science

    Shipping end-to-end ML pipelines that integrate with real workflows.

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

    ◦ Engineered forecasting models using Prophet to optimize inventory across 51 hospitals, analyzing usage patterns for 10,000 + items; reduced stockouts by 42% and cut waste by $1.6M annually.
    ◦ Designed a RAG-based system with Llama 3.2 and web scraping to automate item categorization and suggest substitutes; boosted substitution accuracy by 25% and eliminated 200+ hours/month of manual effort.
    ◦ Integrated the Medline API with procurement systems to automate exception handling; reduced purchase order exceptions by 75% and saved 80+ hours/week in manual processing.
    ◦ Directed data migration for an 8-hospital (Ascension-Chicago) acquisition, executing ERP data mapping for seamless Lawson integration; ensured zero data loss during a $370M+ acquisition.
    ◦ Automated contract audits by developing scalable ETL pipelines processing 3M+ rows/day, reducing audit time from 8 hours to 10 minutes and improving data accuracy by 95%.

  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%

Selected work

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