Data Scientist

CliftonLarsonAllen•Madison, WI

About The Position

CLA is looking to hire a Data Science Associate to join our growing Internal IT team. About the role: As a Data Science Associate, you will: This role constructs complex solutions that integrate data wrangling, visualization, and advanced modeling techniques into a seamless workflow using software development best practices in R, Python, or other scripting languages. They are comfortable working with APIs, web scraping, and SQL/no-SQL databases. They automate business workflows while integrating stochastic/numeric algorithms in the process.

Requirements

No experience required.
Bachelor's degree is required.
Combination of relevant experience, education, and training may be accepted in lieu of degree.

Nice To Haves

1 year of experience preferred
Experience with APIs, web scraping, SQL/no-SQL databases, and cloud-based data solutions preferred.
Degree in a field of Statistics, Computer Science, Economics, Analytics, or Data Science (e.g., Informatics, Data Science, Health Data Science) preferred.

Responsibilities

Expand Knowledge/Exposure: Develop service specific knowledge through greater exposure to peers, internal experts, clients, regular self-study, and formal training opportunities. Gain exposure to a variety of client situations to develop business skills. Retain knowledge gained and performance feedback provided to transfer into future work. Approach all problems and projects with a high level of professionalism, objectivity and an open mind to new ideas and solutions.
Data Analysis: Participate in the collection, analysis, and automated collection of data using a variety of data tools. Together with the Data Analytics team, support the building and implementing of models, algorithms, and simulations supporting solutions for external clients and internal projects. Assess the effectiveness and accuracy of new data sources and data gathering techniques.
Data Development: Working under the guidance of a variety of Data Analytics team members, gain exposure to developing custom data models and algorithms to apply to data sets. Gain experience with predictive and inferential analytics, machine learning, and artificial intelligence techniques. Use existing processes and tools to monitor and analyze solution performance and accuracy, and communicate findings to team members and end users.
Collaboration: With specific direction, learn to work individually as well as in collaboration with others. Interaction with others will primarily be virtual with leadership and colleagues from other offices.