Microsoft Data Science Internship Opportunities - Redmond 

United States, Washington 

881017570

Required Qualifications:

• Currently pursuing a Bachelor's Degree or Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field.
• Must have at least one additional quarter/semester of school remaining following the completion of the internship.

Preferred Qualifications:

• Some Engineering experience and or project course work using large data systems on SQL, Hadoop, etc.

• Proficiency using one or more programming or scripting language to work with data such as: Python, Perl, or C#.

• Some experience and or project course work performing data analysis and applying statistics working with tools such as: Excel, R, MATLAB, AMPL, or SAS.

• Some experience and or project course work with product and service telemetry systems.

• Some A/B Testing or experimentation (this can be from conducting real life science experiments, hypothesis testing in stats etc.) Not required but ideal.

• Some experience or course work applying basic ML to a type of data and or used algorithms to conduct experiments on data.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:

• Formulate approaches to solve problems using well defined algorithms and data sources.
• Incorporate an understanding of product functionality and customer perspective to provide context for those problems.
• Use data exploration techniques to discover new questions or opportunities within your problem area and propose applicability and limitations of the data.
• Interpret the results of their analysis, validate their approach, and learn to monitor, analyze, and iterate to continuously improve.
• Engage with peer stakeholders to produce clear, compelling, actionable insights that influence product and service improvements that will impact millions of customers.
• Participate in the peer review process and act on feedback while learning innovative methods, algorithms, and tools to increase the impact and applicability of your results.