Data Scientist

The Role: At Morningstar, helping investors is what brings us together and drives our work. We're looking for a Data Scientist who will be a lead contributor in the implementation of Artificial Intelligence (AI) on various Data Collections processes. You'll work with team members across disciplines developing products for investors. You'll interact with Developers, QA and product managers to understand requirements and craft technical solutions that push us forward. This position is based in our Toronto office.
We're looking for an individual who possesses strong technical skills, an ability to think through problems working with engineers from various disciplines.
Job Responsibilities

• Build well designed, well-engineered, stable, scalable products across a variety of markets
• Good working experience in data analysis & model design using patterns
• Follow software design best practices such as continuous integration, test-driven development, and regular deployments.
• Solve investors' problems with technology, understanding market chaos at work and crafting technically appropriate interactive experiences to communicate effectively with individual investors.
• Be passionate about quality, process, programming, and investing in general, seeing opportunities for improvement, seizing them, and then sharing your findings with others.
• Familiarity or desire to become an AWS certified developer
• Take ownership of the success of the team with a "gets things done" attitude; the ability to understand and leverage new technologies, potentially wearing many hats including business analyst, DBA, QA, and project manager.
• Help to shape product vision.

Qualifications

• 2+ years of experience in the 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).

Nice to have

• 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.

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