Responsibilities: 

• Design & develop enterprise solutions to be flexible, scalable & extensible. 
• Improve complex data flow, data structures and database design to move to next platform. 
• Be a Role Model to the team to collaborate on good object-oriented designs & domain modeling. Enforce good agile practices like test driven development, Continuous Integration. 
• Build solutions that incorporate numerical techniques such as linear algebra, machine leaning, statistics, and optimization. 
• Hands-on development will be an integral part of the responsibilities. 
• Develop areas of continuous and automated deployment. 
• Introduce and follow good development practices, innovative frameworks and technology solutions that help business move faster. 
• Follow best practices like estimation, planning, reporting and improvement brought to processes in every day work. 

Requirements: 

• 1-3 years of experience in data science field 
• An advanced degree in engineering, computer science, statistics or related field is preferred 
• Expertise with either Python or Java is essential, while experience with both is desirable; other programming language skills are highly desirable. Experience with Python packages like pandas, scikit-learn, TensorFlow, numpy, NLTK is a plus. 
• Basic knowledge of statistical/ML/AI algorithms 
• Experience with DevOps tools (e.g. Splunk, Git, uDeploy, Jenkins, Control-M) is desirable 
• Experience with Agile software engineering practices 
• Experience with back-end XML, relational, and file-based databases (e.g. SQL, Postgres, Redshift, Netezza, HDFS) 
• Experience developing and deploying solutions using services in the Amazon AWS ecosystem (Lambda, EC2, RDS, EMR) 
• Experience with the Hadoop stack (MapReduce, Pig, Hive, Nifi, Spark) is desirable 
• Experience with at least one statistical modeling language (e.g. R, MATLAB, Python) 

Intermediate knowledge of statistical methods is desirable 

• Familiarity with common data cleaning and munging techniques 
• Familiarity with automation tools such as Puppet or Chef is a plus. 
• Familiarity with data visualization is a plus (e.g. Tableau, Shiny, d3) 
• Familiarity with statistical software, linear algebra, optimization, and information visualization is a plus. 
• Familiarity with mutual fund, fixed income, and equity data is a plus 
• Fluent in both oral and written English. 

Morningstar is an equal opportunity employer.

Morningstar’s hybrid work environment gives you the opportunity to work remotely and collaborate in-person each week. We’ve found that we’re at our best when we’re purposely together on a regular basis, at least three days each week. A range of other benefits are also available to enhance flexibility as needs change. No matter where you are, you’ll have tools and resources to engage meaningfully with your global colleagues.