Data Scientist
KellyMitchell matches the best IT and business talent with premier organizations nationwide. Our clients, ranging from Fortune 500 corporations to rapidly growing high-tech companies, are exceptionally served by our 1500+ IT and business consultants. Our industry is growing rapidly, and now is a great time to launch your career with the KellyMitchell team.
Data Scientist
Duties:
- Collaborate with data scientists/engineers/stewards on data extraction and data cleaning.
- Develop powerful analytics insights from geographical, manufacturing, industrial data using advanced machine learning techniques.
- Design, implement and optimize OR/simulation/machine learning models based on structured and unstructured data.
- Work closely with the software engineering team in the delivery of analytic software
- Work in a highly interactive, team-oriented environment.
Desired Skills/Experience:
- PhD in Mathematics, Economics, Statistics, Operations Research or a related field with statistics courses/publications/proven expertise (Electrical/Mechanical/Environmental Engineering, Spatial Statistics, Geoinformatics, Environmental Modeling, Engineering, Computer Science, or Computational Biology)
- Or Master's degree plus 3 years of industry experience.
- Proficient in machine learning algorithms and concepts;
- Ability to work in a highly interactive, team-oriented environment.
- Comfort in communicating technical content in an easy to understand way
- Proficiency of at least one of Python or R is a must-have and working knowledge of the other
- Working knowledge of the following programming languages: SQL, Java, Javascript, C/C++, Scala is a plus
- Proficiency in Spatial and/or Temporal Statistical Modeling
- Proficiency in Machine learning algorithms and concepts (Ensembles, Deep Learning, SVM, etc.)
- Experience working with agricultural/biological scientific data is highly desired
- Drive for translating business problems into research initiatives that deliver business value
- Creativity in defining challenging exploratory projects
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