We are seeking an experienced data scientist/programmer who thrives in a challenging environment, is passionate about technology products and applies AI & Machine learning data analytics techniques to build a scalable energy analytics platform. This position will work closely with data science team to help support all facets of the data supply chain involving consumption and analytics.
Innowatts is a Houston based energy technology start-up, focused on building a scalable Energy Analytics, Segmentation and Engagement Platform. Innowatts is enabling utilities to optimize energy cost and empower energy consumers with personalized energy programs and service.
- Excellent understanding of machine learning techniques and algorithms, such as Neural Network, Random Forest, Gradient Boosting, Linear and Logistic Regression, SVM, Data Visualization, Statistical Modeling, Machine Learning, Design of Experiments, Machine Learning and Project Management, etc.
- Experience with common data science toolkits, such as R, SAS, SQL, Hadoop, MapReduce, Tableau and Power Bi
- Experience with AI code and algorithm development and related software AWS lambda function, TensorFlow etc.
- Experience with common big data toolkits i.e. Hadoop, MapReduce etc
- Great communication skills
- Experience with data visualization tools, such as Tableau Minitab, etc.
- Proficiency in using query languages such as SQL, Hive, Pig
- Experience with NoSQL databases, such as MongoDB, Cassandra, HBase
- Good applied statistics skills, such as distributions, statistical testing, regression, etc.
- Good scripting and programming skills
- Data-oriented personality
- BS/MS degree in Engineering, with data science specialisty discipline
- At least 3-6 years of data analytics experience in a distributed computing environment
- Selecting features, building and optimizing classifiers using machine learning techniques
- Perform predictive analysis i.e. forecasting, disaggregation, customer churn etc
- Enhancing data collection procedures to include information that is relevant for energy analytic systems
- Processing, cleansing, and verifying the integrity of data used for analysis
- Doing ad-hoc analysis and presenting results in a clear manner
- Creating automated analytics systems and constant tracking of its performance