Data Scientist

Responsibilities:

• At Pull Systems, data science is a core competency. It is at the center of our product, not just a tool to help executives make business decisions. The data science team is composed of motivated data scientists who work together as peers inventing, testing, and refining our core models for EV battery analysis. These models span analytics and discovery, statistical modeling, data mining, and machine learning.
• As a small company of less than a dozen employees, every Pullitician–regardless of role–shares responsibility for a successful product. The data science team embodies this with a combination of individual and collaborative discovery, aligned very tightly with world-class data engineering and software engineering teams to solve end-to-end problems.
• Some example statistical and machine learning methods we use on the data science team include Regression Analysis, Naive Bayes, Random Forests, PCA/LDA/IDA, and Neural Networks and related architectures (CNN, RNN, Transformers, LSTMs).

Qualifications:

• At least 4 years of experience in a professional data science, machine learning, or AI role that worked alongside engineering and software engineering teams. Pure research experience may be qualifying depending on the application.
• Experience in two or more applicable data science disciplines: statistical modeling, machine learning, data mining, time series data analysis, or data engineering.
• Hands-on experience in common machine learning algorithms including random forest, logistic regression, PCA, support vector machines, Neural Networks (convolutional/transformers/recurrent/etc), KNN, KMeans, or equivalent.
• Experience working with engineering teams generalizing and integrating statistical models into software applications at scale, using standard engineering process and tools such as cloud platforms, source control, project management, and bug tracking.
• Expertise in Python or R and modern data science packages.
• Demonstrated ability to work independently on data science problems: designing, training, iterating, and validating your own models against loose directives set by management.
• Proven ability to communicate your methodologies and findings to a wide range of audiences from fellow scientists to company executives.