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Civis Analytics: Data Scientist, Statistics


Be part of the Research and Development team, responsible for developing the fundamental data science methods, techniques, and best practices that power the mission of our company, performing predictive analytics, algorithm development, experimental design, visualization, and survey research.



Civis Analytics
Company: Civis Analytics
Location: Chicago, IL
Web: civisanalytics.com
Position: Data Scientist, Statistics

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Who We Are

Civis Analytics is building a data-driven world. We create technologies that empower organizations to unlock the truth hiding in their own data—transforming them into smart organizations that are ready to thrive. We build cloud-based products to help organizations do data science better and easier.

We're solving the world's biggest problems with big data. Through research, experimentation, and iteration, we’re transforming how organizations do analytics. Our clients range in scale and focus from local to international, all empowered by our individual-level, data-driven approach.

While our history is rooted in political campaign targeting, we’re now partnering with intelligent organizations in healthcare, media, education, and a range of other domains. We are a community of of engineers, data scientists, and statisticians looking to add to our growing team. We are smart, fun, and a little bit weird. Does this sound like you?

Position Overview

The Research and Development team is responsible for developing the fundamental data science methods, techniques, and best practices that power the mission of our company. Our diverse work includes predictive analytics, algorithm development, experimental design, visualization, and survey research. As a data scientist on our Chicago-based R&D team, you will work closely and collaboratively with analysts and engineers to develop and operationalize the techniques that quantify and solve big, meaningful problems. Our team dives deeply into big problems in a variety of areas.

With a specialization in statistics, this data scientist role will apply state-of-the-art statistical methods to our research as well as develop entirely new methods to use our data in new ways. You will be a critical voice on determining which statistical approaches are appropriate for which problems – whether in the areas of modeling, analysis, survey methodology, or experiment design. The new methodological approaches you develop will be incorporated into Civis Analytics’ core products and put in the hands of our clients to solve real business problems. Then we learn from their usage and continually improve.

We are looking for individuals from a diversity of backgrounds with demonstrated quantitative and problem-solving skills. We value creativity, hard work, and on-the-job-excellence and offer competitive compensation and benefits packages. In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States.

Minimum Qualifications

  • Master’s degree in statistics, machine learning, physics, computer science, or a Bachelor’s degree and outstanding work experience
  • Comprehensive knowledge of modern statistical learning methods
  • Experience using applied statistics or machine learning in a professional or other intensive problem-solving environment with large, complex datasets
  • Expertise in statistical packages such as R or the Python scientific stack (NumPy, SciPy, scikit-learn, pandas)
  • Experience programming in a Linux environment
  • Experience identifying and correcting for problems in imperfect data
  • An ability and eagerness to constantly learn and teach others

Preferred Qualifications

  • PhD in statistics or a comparable field
  • Ability to expand upon modern statistical learning methods
  • Significant work experience in statistical modeling or machine learning
  • Experience with survey methodology
  • High proficiency in programming with Python, Ruby, Java, or similar languages
  • Experience with SQL databases
  • Expert ability to identify and adapt to imperfect data

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