Data Scientist

Pull Systems helps electric vehicle (EV) manufacturers, suppliers and operators manage their fleets with increasing levels of automation. Our software platform is underpinned by a library of machine learning models that predict battery behavior using a world class database of 3 years of operating history from nearly 100k vehicles. With Pull Systems, our manufacturer customers optimize costs (e.g., by predicting battery maintenance), boost aftersales revenues (e.g., by offering warranty extension products) and improve consumer experiences (e.g., by automating charging workflows).

Pull Systems is well-funded by some of the best-known investors in mobility, spun out of the Up Labs incubator in 2023 as the flagship company formed in partnership with Porsche.

Responsibilities

1. 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.
2. 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.
3. 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).

Requirements

1. 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.
2. Experience in two or more applicable data science disciplines: statistical modeling, machine learning, data mining, time series data analysis, or data engineering.
3. 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.
4. 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.
5. Expertise in Python or R and modern data science packages.
6. Demonstrated ability to work independently on data science problems: designing, training, iterating, and validating your own models against loose directives set by management.
7. Proven ability to communicate your methodologies and findings to a wide range of audiences from fellow scientists to company executives.

Additional Information

1. An advanced degree and academic research experience in data science, machine learning, or AI.
2. Experience working with data from vehicles.
3. Experience working with data from high-voltage battery systems.
4. Experience with covariate time series data models, especially applying them in deep learning systems.
5. Professional experience with companies in the mobility sector.
6. Experience in general software engineering, especially backend services.
7. Experience in and passion for building things from scratch with small teams.