Steven Calder, Data Analytics Developer in Charlotte, NC, United States
Steven Calder

Data Analytics Developer in Charlotte, NC, United States

Member since March 7, 2018
Steven is a versatile data scientist who can connect concepts from various fields of thought to create novel solutions and identify new areas to apply data. His work emphasizes clear communication of technical concepts via interactive visualizations and narrative storytelling, allowing decision-makers from all backgrounds to understand and take appropriate actions.
Steven is now available for hire




Charlotte, NC, United States



Preferred Environment

PyCharm, Tableau, pgAdmin

The most amazing...

...use of data has been to combine data from various railway sensors to create predictive maintenance products, with multiple patents along the way.


  • Data Scientist/Technical Lead

    2016 - 2019
    • Recruited and led a technical team of 12 data scientists, engineers, and analysts.
    • Built a data platform for real-time and historical analytics of rail data.
    • Authored numerous patent applications for new data-driven products related to rail automation.
    Technologies: Python, AWS, PostgreSQL, Tableau
  • Head of Data Sciences

    2015 - 2016
    CrossEngage GmbH
    • Recruited and led a team of database engineers and analysts.
    • Created a system to segment customers and lead them through the sales pipeline.
    • Presented new data products and inventions to various stakeholders and funders.
    Technologies: Python, PostgreSQL, Cassandra, Spark, R
  • Postdoctoral Researcher

    2012 - 2014
    Wageningen University and Research Centre
    • Led a team of doctoral students in developing a research plan.
    • Created and analyzed the data from "artificial noses" as a diagnostic instrument.
    • Authored research and review articles in top journals.
    Technologies: Python, MatLab


  • Rail Vehicle Anomalies (Development)

    Through the examination of data from sensors on a moving train, we have identified a set of different types of anomalous readings. These types can be visualized to explain the different conditions or locations where these anomalies occur.


  • Libraries/APIs

    Pandas, NumPy, Scikit-learn
  • Tools

  • Other

    Data Analytics, Data Visualization, Time Series Analysis
  • Languages

    SQL, Python
  • Paradigms

    Data Science
  • Storage



  • Ph.D. in Chemical Physics
    2008 - 2011
    University of Minnesota - Minneapolis, MN
  • Master's degree in Chemistry
    2006 - 2008
    University of Minnesota - Minneapolis, MN
  • Bachelor's degree in Chemical Physics
    2003 - 2006
    College of William and Mary - Williamsburg, VA

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