BlocPower is a clean-tech company backed by Silicon Valley investors such as Andreessen Horowitz and Kapor Capital. Meaning, our company must build innovative technologies that scale up our venture nationally and globally. That is also why that major organizations – from public to private – are investing in us and supporting what we do:
US Department of Energy invested $1.9m in BlocPower to develop a crowd-sourcing website to help market, finance, and install energy efficiency retrofits for small buildings.
We are raising a $8m loan fund with Goldman Sachs to invest in retrofit projects to make buildings healthy and more energy efficient.
Data analytics play a crucial role in our operation. We are looking for a Data Analysis Intern that will help in discovering the information hidden in vast amounts of data, and help us make smarter decisions to deliver even better products. You, as our data analysis intern, will be required to develop expertise in the application of data analytics (e.g. machine learning and artificial intelligence) in building science, energy efficiency and renewable energy. You will engage in regular brainstorming and team planning sessions as we come up with out-of-the-box solutions for our unique set of challenges.
Working with team in selecting features, building and optimizing classifiers using machine learning techniques to solve our challenges;
Enhancing data collection procedures to include information that is relevant for building analytic systems;
Processing, cleansing, and verifying the integrity of data used for analysis;
Doing ad-hoc analysis and presenting results in a clear manner.
Skills and Qualifications:
Understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM;
Decision Forests, etc.
Experience with common python data science toolkits, NumPy, Pandas, and Scikit-learn;
Experience with data visualisation tools, such as D3.js, matplotlib;
Proficiency in using SQL;
Experience with NoSQL databases, such as MongoDB(preferred), Cassandra, or HBase;
Good applied statistics skills, such as distributions, statistical testing, regression, etc.
Good python scripting and programming skills. Experience using and developing APIs a plus;