The company is looking to solve the problem of predicting the adoption of Electric Vehicles by customers and understanding their energy consumption patterns.
Requirements
- Proficiency in programming languages such as Python and/or R
- Experience with spatio-temporal statistics, geospatial data analysis, and machine learning techniques for time series with large datasets
- Experience designing, developing, and maintaining scientific code that runs at scale
- Strong understanding of applied statistics and probability
- Experience with handling large datasets and cloud computing platforms (e.g. AWS, Azure, GCP, or other enterprise level analytics platforms)
- Experience with data science standards and processes (model building and evaluation, optimization, feature engineering, etc.)
- Experience writing software to extract features from time series data or large-scale datasets
Responsibilities
- Design and develop production-quality scientific algorithms in Python to extract patterns of customer energy consumption
- Develop spatio-temporal algorithms to predict adoption of Electric Vehicles by customers
- Perform in-depth validation of algorithms driven by business and technical requirements
- Perform deep root-cause analysis, EDA, and error analysis of the ML models
- Develop and maintain scientific code that runs at scale
- Extract features from time series data or large-scale datasets
- Communicate technical information and implications to peers and business partners
Other
- Ph.D. in Engineering, Computer Science, Physics, Econometrics or Economics, Mathematics, Applied Sciences, Statistics, or other highly quantitative discipline
- Excellent problem solving and communication skills
- Collaborate with members of your team and with domain experts to understand practical implications of your model and deliver results to business partners
