• The ideal candidate in this role should minimally have either:
• A Bachelor’s Degree with course work in analytics, statistics, computer science, informatics, and/or mathematics and 2+ years of experience and passion for leveraging data to drive significant organizational impact, or
• a Master’s Degree with course work in analytics, statistics, computer science, informatics, and/or mathematics, or
• 4+ years of experience and passion for leveraging data to drive significant organizational impact.
• Considerable knowledge using computer languages (R, Python, SQL, etc.) to manipulate and draw insights from large data sets as well develop software for automation
• Broad knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications
• Broad knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages and drawbacks
• Strong understanding of relational and dimensional databases, theories, principles, and practices
• Exceptional analytical, conceptual, and problem-solving abilities
• Must inhabit strategic thinking
• Strong written/oral communication and presentation skills
• Resourceful self-starter and highly motivated team player
• Able to perform well in a fast-paced environment
• Experience with data manipulation to include cleansing, standardizing, and transforming.
• Experience in leading workshops or training sessions with a user community a plus
• Experience with the following concepts or tools is not a requirement but considered a plus (geocoding and geospatial data, shiny, network diagraming, neo4j, Docker, Kubernetes)
• Experience generating and distributing visualizations to a broad range of audiences
• Effective communicator and someone who enjoys getting to understand nuances of a problem
• Proficiency using frameworks such as Shiny, Dash, Flask, or Streamlit to build user-facing interfaces, connect to backend data pipelines, and deploy lightweight analytic applications.
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