Schneider's award-winning TECH team is looking to hire a Data Scientist to help create, build, and maintain next generation analytics solutions. The focus of this position is to develop practical solutions that help our customers meet their objectives. Using open source and licensed state of the art modeling and visualization tools like R, SPSS, Python, and Tableau, you'll be able to grow your technical skills while working on impactful and innovative analytics solutions. If this sounds exciting to you, review the information below and apply today.

About the role:

• Create proposals, statements of work, project logs and updates, and present in a clear and concise manner, as well as document project assumptions and track savings
• Identify data sources and prepare data (merging data from different sources, cleansing, manipulation, etc.)
• Collaborate with Senior Data Scientists and Engineers to decide on approach and implementation
• Prepare and deliver written non-technical reports, present to managers indicating solutions or range of possible alternatives, solution pros and cons, and make technical suggestions
• Maintain consistent attendance while exhibiting behavior in alignment with our core values

As an associate you will enjoy a strong work/life balance that includes paid vacation and holidays along with paid personal time off. You will also have access to medical, dental, and vision insurance and enjoy our company-paid life insurance and 401(k) savings with a company funded retirement plan. We also offer a results-based incentive pay program where you can earn above and beyond your base pay. Schneider values individual development and offers a wide variety of opportunities through company-paid training and more than 170 personal development courses. What are you waiting for, you will love it here!

Ideal candidate will have:

• Master's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or a related field; or a Bachelor's Degree plus two years of related experience
• Basic knowledge of statistics modeling, solution techniques and tools and the ability to learn and utilize statistical programming languages and tools such as R, SAS, Python, SQL, SPSS, and Tableau
• Working knowledge of the uses and limitations of statistical methodologies such as time-series forecasting, regression analysis, random forests, k-means clustering and Bayesian methods, machine learning, and the ability to select the appropriate methodology to complete assignments
• An ability to effectively communicate technical information and concepts to non-technical audiences