Data Engineering | Blockchain Development

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Hello and thank you for your visit.

After obtaining a dual master’s degree in mechanical engineering and management/ entrepreneurship and a master in Aerodynamics, I worked as a visiting scholar at UC Santa Barbara and UC San Diego. Then I worked as a Solar Project Manager in Paris, France and in San Francisco, California, for a total of 7 years of experience in the Renewable Energy industry.

Over the years, I developed a strong interest in the Internet of Energy, including: Smart Grids, Distributed Generation, Energy Storage, Demand Response, Data Science, Machine Learning and Blockchain technology.

Therefore I decided to focus my career completely on Data Engineering and Data Analytics (3 years of experience as of today). In addition to my interest in energy,  I remain interested in various industries (transportation, robotics, blockhain &dapps/smart contracts…).

I am currently a Data Engineer with Deepki, where my main activities are: data integration, data pipeline, data collection, data cleaning and data visualization. The stack includes: Python/Flask/Pandas, VueJS, MongoDB,  InfluxDB, Docker, Jenkins

Feel free to check out my portfolio and to contact me (via LinkedIn) with any questions.

Best regards,

Nicolas Guillaume




Recent Projects and Education


  • Projects:
    • Smart Contracts on the Ethereum blockchain (Angelhack/Consensys hackathon)
    • Smart Contracts on the Hyperledger blockchain (Datachain hackathon)
  • Online courses:
    • How to build Ethereum decentralized applications : Truffle – Ganache – Metamask -Web3 (
    • Build a fully decentralized application with IPFS (
    • Deploy Smart Contracts (1/2) : Remix – Ropsten Ethereum Network – Infura (
    • Deploy Smart Contracts (2/2) : Geth – Rinkeby Ethereum Network – Truffle (


  • Projects:
    • Challenges: 3rd prize at Engie DataPower Hackathon (July 2016), Kaggle and projects
    • Web development: design and coding of a website featuring a personalized recommendation engine for cooking recipes (, programmed in Python/Flask/Numpy/Scikit-Learn/NLTK
    • Creating a smart power meter, detecting devices, and analyzing usage patterns (Project)
  • Online courses:
    • Machine Learning (Stanford University – Coursera)
    • Exploratory Data Analysis with R (Udacity)
    • Foundations of Marketing Analytics (ESSEC Business School – Coursera)
    • Foundations of Strategic Business Analytics (ESSEC Business School – Coursera)
    • Digital Analytics Fundamentals (Google Analytics)
    • SQL (Stanford Online)
    • A/B Testing (Udacity)
    • Statistical Inference (Johns Hopkins University – Coursera)
    • Statistical Inference (Udacity)
    • Descriptive Statistics (Udacity)


  • Project (4-month internship)
    • Predicting the demand of a bike sharing system
  • Online course
    • Introduction to Data Science (University of Washington – Coursera)


  • Python (Codecademy)




Data Science Skill Set

  1. Data Cleaning
  • Importing data
  • Joining multiple datasets
  • Detecting missing values
  • Detecting anomalies
  • Imputing for missing values
  • Data quality assurance
  1. Exploratory Data Analysis
  • Ability to formulate relevant questions for investigation
  • Identifying trends
  • Identifying covariation between variables
  • Communicating results effectively using visualizations (scatterplots, histograms, box and whisker, etc.)
  1. Interactive Data Visualizations
  • Including metrics (KPIs) relevant to customer’s needs
  • Creating useful features
  • Creating interactive online dashboards
  • Generating reports or other automated actions
  1. Machine Learning
  • Reasons behind the choice to use a specific machine learning model
  • Selecting the right evaluation metrics (AUC, adj-R^2, confusion matrix, etc.)
  • Feature engineering and selection
  • Hyperparameter tuning
  • Curve Clustering, Anomaly Detection, Timeseries
  • Microservices Architecture: Enterprise Web Services (API) with Neural Networks, etc.
  1. Communication
  • Know intended audience
  • Present relevant visualizations
  • Tie results to a business impact (reduced cost, increased revenue)







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