Lead Data Scientist
| Company | Resideo |
|---|---|
| Location | Golden Valley, MN, USA |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | |
| Experience Level | Expert or higher |
Requirements
- 10+ years of related industry experience
- Experience using Python, R, etc. working with (preferably) time-series data to support data analysis, visualization, exploratory data analysis, feature generation, and model fitting (in addition to other common analysis activities common to machine learning)
- Expert in at least one programming language for data analysis (e.g., Python, R), experience with SQL a plus
- Strong foundational knowledge in project management with the ability to work in a fast-paced, high-visibility environment
- Industry experience with developing and applying machine learning and statistical modeling in at least one of the following categories: Anomaly detection, Time-Series Methods, and/or Image processing (e.g. convolutional neural networks, auto-encoders)
Responsibilities
- Analyze and interpret complex time-series data from connected HVAC systems to build cutting-edge metrics of HVAC performance, generate insights, and create value for our network of pros and homeowners using our IoT solution
- Develop methodologies and experiments to continuously improve predictive and machine learning algorithms
- Help develop end-to-end process machine learning solutions to support program growth and expansion goals
- Working with cross-functional teams, including Product Managers and Software Engineers, identify critical business problems and develop solutions to support data-driven business decisions
- Work to democratize data by building and socializing decision tools (e.g., reports, data products, dashboards)
Preferred Qualifications
- Experience with HVAC systems or related industrial applications
- Experience with IoT sensor data, preferably in the time-series space, working with edge data processing and connected device ecosystems
- Experience working with Apache Spark (preferably PySpark)
- Familiarity with modern ML frameworks and libraries including, deep-learning (e.g., TensorFlow, Keras, or PyTorch), Scikit-learn, Numpy, Pandas, and MLFlow.
- Knowledge and experience with Databricks, Jupyter notebooks, Git, AWS or Azure cloud environments
- Detail oriented and willing to learn new skills and tools
- Experience working with cross-functional teams and an ability to communicate clearly and effectively to technical and non-technical audiences
- A continuous learning mindset with a willingness to stay updated with the latest trends and technologies in data science and machine learning.