This position requires improving and supporting our applications infrastructure, writing code, analyzing data, creating models, creating ad-hoc apps/reports/dashboards, working with both internal and external customers, participating in project management and execution, and leading small individual and team projects. The Advanced Analytics team members are passionate about continuous learning and turning that knowledge into cutting-edge analytical solutions. We strive to deliver innovative analytical solutions that generate tangible business results.

Key functions include but are not limited to the following:

Develop, test, deploy, and help monitor analytics products and predictive models including data engineering, data analysis, and AI/Client/NLP.

Service the analytical needs of internal and external customers by providing high-value analysis, reports, dashboards, and/or electronic files as needed.

Execute and participate in leading high-value data science projects including defining project deliverables, estimating timelines, estimating expected business value, assigning project tasks, and ensuring efficient execution and proper documentation from ideation through business integration.

Help implement automated solutions to monitor and maintain the performance of the team's analytical products. Identify when model retraining or new solutions are needed.

Design and execute well-documented experiments to identify Client uses of internal and external data to deliver business value.

Keep up to date with AI and Client best practices as relates to MEC's initiatives and share key insights with the team.

Help design, evangelize, and continuously improve group best practices.

Conduct formal peer reviews of data science projects from other team members.

Work on cross-functional teams within MEC and across the larger corporation to develop and implement Client analytics and data science solutions.

Provide mentorship to junior team members.

Create and deliver presentations to internal and external customers as needed.

Qualifications

Bachelor's or Advanced degree in data science, computer science, mathematics, statistics, engineering, physics, or other sciences (or college degree with significant technical experience in a corporate setting).

Education should include significant work in advanced mathematics, statistics, and/or data science.

Undergraduate or graduate work in computer science is a plus.

Requires 4 to 7 years of business experience as a data scientist or in a related field.

Possesses an intense curiosity and determination to explore and solve complex business problems utilizing data, technology, and analytical techniques.

Ability to multitask and manage assigned work under very tight deadlines.

Advanced understanding of a variety of statistical and machine learning algorithms.

Intermediate-to-Advanced understanding of natural language processing.

History of following data visualization best practices.

History of following coding best practices.

Experience in breaking projects down into deliverables, setting timelines, and providing accurate status reports to management.

Significant experience working with large databases for analytical purposes. This would include both transactional systems and data warehouse systems.

Intermediate-to-Advanced level understanding of SQL Databases and the SQL Programming Language SQL Server/Oracle and including programming in T-SQL, PL/SQL or the like.

Intermediate-to-Advanced level programming experience with Python and/or R.

Knowledge of information technology practices related to data, data warehousing, ETL processes, and data mining.

Understanding of project management practices and approaches including waterfall and Agile methodologies and experience with project management software such as Microsoft Team Foundation Server, Jira, Trello, etc.

Experience with code management (version control) techniques utilizing tools such as Microsoft Team Foundation Server, GitHub, etc.

Intermediate experience with UNIX and Docker.

Excellent listening and interpersonal skills.

Experience working in a team-oriented, collaborative environment.

Occasional evening and weekend work to meet deadlines.

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