Camille Girabawe, Statistics Developer in Santa Clara, CA, United States
Camille Girabawe

Statistics Developer in Santa Clara, CA, United States

Member since July 27, 2019
Camille is a data-driven thinker and strategies with a Ph.D. in Physics from Brandeis University and professional experience in data science in the B2B and B2C world. He has data science, software development, and business skills to design, implement, and deploy on-premise or on-cloud AI solutions for business problems. Camille enjoys applying machine learning to different areas to solve real life challenges.
Camille is now available for hire

Portfolio

  • Adobe
    Machine Learning, Deep Learning, Statistics, Python, SQL, GCP, Airflow...
  • SAP Labs
    Machine Learning, Deep Learning, Statistics, Python, SQL, JavaScript, GCP...
  • Brandeis University
    python, matlab, comsol, arduino, microscopy

Experience

Location

Santa Clara, CA, United States

Availability

Part-time

Preferred Environment

MacOS, Linux, Atom, Git, HanaStudio

The most amazing...

...thing I've designed and implemented was an AI-driven app in S4HANA Procurement suite to propose materials for contract negotiation based on historical spending.

Employment

  • Senior Data Scientist

    2019 - PRESENT
    Adobe
    • Developed AI-driven filters to help marketers to extend their audience using historical and real-time data of campaigns' success and failure. Results are a lift of up 25% on the audience and a boost of about 7% on the success rate.
    • Led the development of a machine learning solution to optimize the right time to send marketing emails in order to increase the open rate. Current A/B testing results show a double open rate.
    • Led AI projects for the conversation marketing platform. From text representation models to language models and natural language understanding, NLP, and computer vision.
    Technologies: Machine Learning, Deep Learning, Statistics, Python, SQL, GCP, Airflow, Newrelic, InDesgin
  • Data Scientist

    2017 - 2019
    SAP Labs
    • Developed real-time monitoring of procurement expenses to propose materials for (re)negotiated contracts. Procurement strategic purchasers can be able to reduce the processing time from an average of two months to three days.
    • Developed a machine learning model to assign a risk score to each purchase requisition in order to automatically approve it based on SAP WorkFlow data. Improved on data consistencey and reduced the approval time-interval to seconds.
    • Developed a machine learning solution for invoice-to-account matching to reduce the processing time, improve the consistency and reduce related accounting errors/frauds.
    • Led the development of a compliance tool. Gathered daily news about a given company, curate, and label each news article to the type of risk or opportunity with respect to compliance. Each company would be given a number of scores on a dashboard to inform the compliance specialists about what actions to make or advice to offer to the executives.
    Technologies: Machine Learning, Deep Learning, Statistics, Python, SQL, JavaScript, GCP, InDesign, TensorFlow
  • PhD - Research Assistant

    2013 - 2017
    Brandeis University
    • Investigated synchronization in non-linear oscillators using the Belousov-Zhabontisky reaction as experimental medium. My work consisted of designing experiment, data collection, data analysis, and mathematical modeling.
    • Built a computer vision and mechanical empowered robotic system that could autonomously control all of the experiments. Reactions were generated in droplets and deposited on microlithographic chips.
    • Developed a programmable illumination microscope controller to excite or inhibit droplets using different light colors. This was achieved by using computer vision technology to track droplets and their status in real-time.
    Technologies: python, matlab, comsol, arduino, microscopy

Experience

  • Programmable Illumination Microscope (PIM) Controller (Development)
    https://www.youtube.com/watch?v=BsRsiweTfp0

    Python-based app to control a multipoint focused microscope to run a light-sensitive experiment. Given a sample of light-sensitive and optically oscillatory solution compartmentalized on a 2D grid, the goal was to focus light on selected cells in order to excite or inhibit them such that the entire grid would be trained in unison (just like fireflies) or any other given structure.

    A combination of deterministic and machine learning models was implemented in Python to train a model that would learn the temporal oscillations of the chemical solution and determine which cells to inhibit/excite by exposing them to light.

    This was part of my dissertation: https://search.proquest.com/openview/aa8113b66c0fcb2d9a4f97fe7cfc5091/1?pq-origsite=gscholar&cbl=18750&diss=y

  • Predicting Green Taxi Tips (Development)
    https://github.com/kthouz/NYC_Green_Taxi

    The goal of the project was to build a model that can predict the amount of tip a driver of a Green Taxi would receive at the end of his/her ride in NYC.

    Data were obtained from the TLC Trip Record Data. After a deep analysis of features for statistical significance, two random forest models were optimized and combined to predict the tip with an MSE of about 14. Several features were revealed to be very significant such as whether a rider pays with cash or electronically, trip duration, and speed which would give an idea of traffic congestion.

    https://camillegirabawe.shinyapps.io/nycgreentaxi/

  • Scoring Model for a Toptal Client (Development)

    Built a machine learning model to score participants of classes for a Toptal client. A model was built using multivariate linear regression algorithms. Since the client expects to gain a larger audience, the models were regularized to overcome any source of overfitting.
    Tech Stack: Python, MongoDB, Node.js.

  • EDA Tool (Development)
    https://www.youtube.com/watch?v=Q62jB0ZFv6M&t=1s

    I built an exploratory data analysis (EDA) tool that can be used to visually explore a dataset, run statistics on it, and add comments in real-time which can be saved or printed later. I used Python-Flask in the back end and D3.js on the front end.

Skills

  • Languages

    Python, SQL, R, JavaScript
  • Libraries/APIs

    Pandas, SciPy, Scikit-learn, TensorFlow, Keras, Dask, Selenium WebDriver
  • Industry Expertise

    Physics
  • Other

    Machine Learning, Mathematical Modeling, Linear Algebra, Statistics, Data Engineering, Deep Learning, Software Development, Data Visualization, Natural Language Processing (NLP), Webcrawling
  • Paradigms

    Data Science, Automated Testing
  • Storage

    MySQL, MongoDB
  • Frameworks

    Flask
  • Tools

    InDesign CC, BigQuery, MongoDB Shell
  • Platforms

    Google Cloud Platform (GCP), Linux, Unix

Education

  • Ph.D. in Physics
    2011 - 2017
    Brandeis University - Waltham, MA

Certifications

  • Computational Investing - Credential ID PPQHXX8CRWV7
    JULY 2016 - PRESENT
    Coursera

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