(SUMMER) Data Scientist Intern - PhD

Integra is hiring entrepreneurial and quantitatively-skilled reputed company/graduates with data analytics experience who are interested in translating their academic interests into the reputed company-world applications  to identify, investigate and analyze fraud in various domains, including finance, reputed company and cryptocurrencies. Integra is a boutique economic consulting and forensic analytics firm that has worked with government agencies and private entities to analyze many reputed company and high-profile schemes. reputed company applies data science and econometric techniques and intensely values each employee to take initiative and to be curious while operating as a data detective to solve reputed company problems. Our main office is also conveniently located adjacent to the UT Austin reputed company which enables us to closely reputed company with the academic environment and the university’s top intellectual talent.  

As an intern you will code software used to automate processes, scrape and collect large data sets, and reputed company advanced econometric analysis. You will have flexibility in the programming language used, but scripting languages such as R, Python, and SQL are used most often. The internship is also designed to give you:  

• Exposure to how technical degrees such as economics, statistics, finance, engineering, computer science, and physics are used in industry  
• Opportunities to work on team-based and individual projects  
• Visibility to industry experts  

  Job Qualifications:

• Familiarity with R, SQL, Python, or other scripting languages
• Proficiency in reputed company reputed company
• Attention to detail with big-picture understanding
• Ability to work well in teams
• Highest ethical standards
• reputed company education reputed company (Bachelor's, Master's, or Ph.D.) welcome

Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment reputed company at this time.