As a Data Scientist at Redaptive, you will have the opportunity to apply machine learning and data science methodologies to help our customers lower their carbon emissions and meet their sustainability goals. You will leverage data, analysis, and Redaptive s extensive metering portfolio to identify and resolve challenges in driving energy-efficiency to scale.
This role will join a team of data scientists with a diverse set of backgrounds and professional experiences. The ideal candidate for this position has a positive mindset, is not afraid to ask questions, and has a high sense of ownership over their work.
Job Responsibilities
- Ideate, propose, and deploy machine learning projects for electric, water, and/or gas resource consumption, savings, anomaly detection, time series classification, and other sustainability initiatives.
- Support time-sensitive questions and analyses from internal teams on product performance, trends, and anomalies.
- Support internal initiatives to identify and implement process improvements and automation within data collection, analysis, and machine learning workflows.
- Implement data pipelines to collect, clean, and preprocess data from various sources, ensuring data quality and integrity.
- Communicate findings and insights to technical, non-technical, and senior stakeholders through clear visualizations, reports, and presentations.
- Acquire and apply domain knowledge of Redaptive s products and software stack to identify and drive the resolution of data inconsistencies, improve model performance, and influence product strategy.
Job Requirements
- Proficiency writing readable, testable, and maintainable Python code and working within Pythons data ecosystem (Jupyter, NumPy, Pandas, etc.)
- Deep expertise with querying relational databases (SQL), non-relational databases (NoSQL), and RESTful APIs
- Proficiency building and validating predictive models within the Python ecosystem (scikit-learn, XGBoost, TensorFlow, PyTorch, etc.).
- Ability to take data science and machine learning solutions into a production environment, deploying models (ideally on cloud architecture like AWS, Azure, or GCP), integrating with software systems, retraining and maintenance of production models
- Ability to collaborate through a version control system (Git, GitHub, GitLab, etc.)
- Self-starter able to thrive in a startup environment and work with a high degree of autonomy and positive mindset
- Excellent storytelling skills including the ability to tailor and deliver presentations on complex data analyses to both technical and non-technical audiences
- Strong communication skills and presentation skills
- Tendency towards curiosity and experimentation with a strong attention to detail and business sense
Education and Experience
- Bachelor s degree (or higher) in statistics, economics, STEM, or similarly quantitative field
- 3+ years of professional data science experience
- Experience working with IoT time-series data
- Experience with infrastructure as code (terraform) and CI/CD platforms (circleCI, travisCI) is a plus
- Experience working with building efficiency, HVAC systems, and/or energy, water, and gas resources is a plus
