Part-Time Data Analyst with software and analytics firm focused on energy efficiency

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Residential Energy and Water Intelligence (Res-Intel) is a Portland, Oregon-based software and analytics firm with an existing portfolio of large utility clients looking for innovative energy solutions. We integrate big data analytics with behavioral tools to drive behavioral change, making the building sector more equitable and energy efficient. Res-Intel is looking for a part-time Data Analyst for up to 20 hours per week of remote work. Workload depends largely on the timing of client deliverables and will typically range from 10 to 20 hours in any given week. The analyst will assist with one or more of the following activities: cleaning and running quality checks on client data, producing visualizations, developing data pipelines, developing regression/machine learning models, and/or managing data resources on Amazon Web Services. The Data Analyst will work with Res-Intel’s Data Scientist(s) and Chief Technology Officer to help generate analytics, draft research plans, memos and protocols for Res-Intel’s clients. The Data Analyst should also possess a strong QA/QC ethic. Specific duties depend partly on the qualifications of the candidate, and there is room for growth. Candidate Qualifications: Required: • Highly skilled with R or Python (R preferred) • Strong statistics/econometrics background Preferred*: • Aptitude for geospatial analysis using R or Python or ArcGIS • Interest in machine learning, deep learning • Familiarity with GitHub • Interest in energy efficiency technologies and programs *Preferred qualifications are not binding, but an ideal candidate will possess at least two. Compensation: $30/hour starting pay for internal work and $60/hour for client work (most hours will be client work). Due to our cybersecurity agreements with clients, all team members need to undergo a background check and complete security training. When submitting please attach your resume.

Keyword: Python Development

Price: $30.0

Python R Report Econometrics Amazon Web Services ArcGIS Deep Learning Data Analysis Machine Learning