The Group: Morningstar’s Data Science Team is part of the broader Quantitative Research Group. The group is the home of AI, machine learning, and quantitative analytics innovation at Morningstar. The team creates independent investment research and data-driven analytics designed to help investors and investor products. We utilize statistical rigor, technological prowess, and big data sets to inform the methodologies we develop.

The individuals in this group transform our high-quality data, independent research, and technological expertise into well-designed models that power our products to delight our clients. The group collaborates with product management, software development, and data service teams to provide high quality bundled solutions to serve investors.

The Role:  You will work with an interdisciplinary team of researchers, technologists, product owners, and fellow data scientists that focus on Morningstar’s financial planning solution. You will apply the best tools and techniques to large amounts of investment, user, and product data. You will build new models for online learning classification and prediction. You will experiment with new technologies and the latest deep learning models, but not forget about existing and established methodologies.

You are a self-starter and inquisitive. You are always willing to learn new techniques and experiment with various technologies. You will use your quantitative chops to answer questions about users and transaction-level data. You know your way around data and can work alongside our data ninjas. You will improve or build new products by implementing and developing algorithms. The synthesis of your analysis will provide valuable investment insight for individuals, advisors, and asset managers.

Responsibilities:

• Formulate a research plan and lead the “science” from inception to execution with minimal oversight
• Interface and communicate with technical and non-technical individuals
• Ask and answer interesting questions of structured and semi-structured data sources
• Deploy and automate different techniques to pick out unexpected lessons and relationships
• Utilize general data mining techniques such as clustering, classification and association analysis in a distributed multi-source environment
• Implement and augment statistical analyses and modeling techniques such as linear and non-linear regression, time-series, anomaly detection, optimization, multivariate analysis, neural networks and graph models, imputation, SVM, Naïve Bayes, etc.
• Understand and use statistical models and hypothesis tests to support research questions
• Query and visualize large datasets using a variety of tools and techniques
• Work alongside a Machine Learning Engineer, Research Scientists, and Business Product Owners
• Communicate results in clear presentations and written reports

Requirements:

• Bachelors degree in statistics, economics, engineering or similar  field, Masters or PhD preferred
• A minimum of 3-years relevant work experience in data science or software engineering , 5-years preferred
• Knowledge and practical experience with machine learning, text and data mining, optimization, statistics, deep learning, and/or econometrics
• Proficiency with data analysis tools like Python, Matlab, or R
• Preferred experience with Big Data platforms such as Hadoop, AWS, H20, Spark
• Familiarity or willingness to learn deep learning and reinforcement learning
• Familiarity or willingness to learn implementations in TensorFlow and Keras
• Experience with relational (MySQL) and big (Impala, HIVE, SparkSQL) databases
• Preferred experience with “big data” and NoSQL distributed processing platforms and implementation of algorithms to run efficiently on these platforms.
• Proven ability to draw conclusions from data and recommend action
• Willingness to learn new techniques and theories
• Demonstrated leadership skills and self-direction
• Ability to work with product, IT, engineering, data, research, and product in an agile environment
• Preferred (but not required) experience working in financial services

Coding sample required prior to interview. Writing samples or research talk required during the interview process.

This position is based in the Chicago office. Relocation considered.

Morningstar is an equal opportunity employer.

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