Data Scientist

The Accelerator seeks a Data Scientist to bolster our data team and provide insight in the data we collect.

The Accelerator at Princeton includes a portfolio of multiple planned independent and intersecting tools. Reporting to the Head of Data Science and Data Engineering, the data scientist will work within our team to help drive data science and data engineering initiatives and collaborations. They will play a crucial role in using data-driven approaches to drive innovation, solve complex engineering problems, and ultimately advance scientific research. They will work on problems that have no precedent and little source material, requiring novel solutions. They will also be responsible for working with the other teams within the Accelerator and our external partners to help foster collaboration and create an incredibly impactful environment for our users.

This is a 6-month term role with potential for extension.

Strategy

• Work closely with the Accelerator leadership team to align data science initiatives with overarching goals and long-term vision.
• Identify and prioritize development projects that benefit from data science methodologies and innovations.

Data Analysis and Modeling 

• Apply advanced statistical analysis, data mining, and machine learning techniques to analyze complex engineering datasets.
• Design, develop, and optimize predictive models, simulations, and algorithms to solve key engineering and scientific challenges.
• Drive the development and deployment of data-driven products and solutions, ensuring alignment with strategic goals.
• Ensure the accuracy, integrity, and quality of data to be made available through the Accelerator.
• Collaborate with data engineering teams to optimize data workflows, enhance data accessibility, and improve performance of modeling systems.

Data Engineering 

• Understand the end-to-end data lifecycle and its effects on machine learning models, actively enhancing and optimizing data pipelines for improved model outcomes.
• Ensure the accuracy, integrity, and quality of data to be made available through the Accelerator.
• Develop data pipelines, automation systems, and scalable infrastructure for real-time and batch processing.

Research and Collaboration 

• Work effectively in a modern, professional software and data engineering environment with a strong understanding of Agile concepts and practices.
• Modern Software Engineering Foundations: agile (Scrum), DevOps, CI/CD, and pair programming, with working knowledge of cloud compute platforms to support collaborative, scalable, and efficient development. 
• Stay current with the latest advancements in data science, machine learning, and relevant engineering fields to continuously innovate.
• Collaborate with research teams to apply state-of-the-art techniques to ongoing scientific challenges.
• Build strong relationships with external partners, driving collaborations that enhance the Accelerator’s scientific impact.

ESSENTIAL 

• 3+ years of relevant work experience as a frontline data scientist, with a record of building innovative solutions.
• Experience working in a remote, agile environment.
• Bachelor's degree or equivalent in a relevant field
• Strong communication and interpersonal skills to effectively collaborate with researchers in the field, other engineers at various levels of experience, and administrative and leadership team members.

PREFERRED

• Background in engineering principles and familiarity with various engineering disciplines.
• Publications in reputable scientific journals or conferences is desirable.

Princeton University is an Equal Opportunity Employer and all qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity or expression, national origin, disability status, protected veteran status, or any other characteristic protected by law.

The University considers factors such as (but not limited to) scope and responsibilities of the position, candidate's qualifications, work experience, education/training, key skills, market, collective bargaining agreements as applicable, and organizational considerations when extending an offer. The posted salary range represents the University's good faith and reasonable estimate for a full-time position; salaries for part-time positions are pro-rated accordingly.

If the salary range on the posted position shows an hourly rate, this is the baseline; the actual hourly rate may be higher, depending on the position and factors listed above.

The University also offers a comprehensive benefit program to eligible employees. Please see this link for more information.