Data Science and Machine Learning Engineer

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** About the Opportunity
**** About the Institute for Experiential AI and Northeastern University
** We have launched a pioneering research and innovation hub in AI—one that will shape the way humans and machines collaborate for decades to come. Led by Prof. Alan Mislove, the Institute for Experiential AI is built around the challenges and opportunities made possible by human-machine collaboration. The Institute provides a framework to design, implement, and scale AI-driven technologies in ways that make a true difference to society.

Our ability to respond to the opportunities afforded to society will depend on training and building a workforce that is AI-capable and prosperous.

Founded in 1898, Northeastern is a global research university and the recognized leader in experience-driven lifelong learning. Our world-renowned experiential approach empowers our students, faculty, alumni, and partners to create impact far beyond the confines of discipline, degree, and campus.
** The Culture
** Here at the Institute for Experiential AI (EAI) we are committed to the highest standards in all that we do. Working at the EAI offers opportunities, an environment, and a culture that just aren’t found together anywhere else. This is the right place for you if you’re curious, motivated by the future of technology, and want to be part of a unique and diverse community that works on high-impact research, educational, business, and societal problems.
** Position Summary
** The Data Science and Machine Learning Engineer will report to Ayan Paul, Research Scientist at EAI and collaborate with Scientists and Clinicians from Peter Castaldi’s group at the Harvard Medical School, John Platig’s Network Biology group at University of Virginia and Jennifer Dy’s Machine Learning group at ECE, Northeastern University and Predrag Radivojac’s group at Khoury College, Northeastern University. There will also be opportunities to work on industry collaborations.

Responsibilities will include building an ETL and ML pipelines for multiomics or healthcare data, write code for data analysis and post-processing data. Training of models like CNN, RNN, Transformers with some work in classical machine learning with XGBDTs is expected. Relevant work can lead to co-author publications and contributions to grant proposals. Tentative start date:
February 2026 for the Spring 2025 semester with possibilities of renewal. This work will contribute towards drug discovery, protein structure and function prediction and understanding disparities in healthcare.
** Qualifications*
* ● MS in Data Science, Machine Learning, Artificial Intelligence, or related fields or currently enrolled in a master’s program.
● A minimum of 3-4 years of experience working with ML/AI (Tensor Flow/Pytorch) and ETL pipelines.
● Proficiency in Python and R. Experienced with Unix and remote computing clusters.
● Having experience in wrangling large datasets, building databases and ETL is preferred.
● An ability to write clean and well-documented code and work with Git Hub repositories.
● Excellent organizational skills including attention to detail and multitasking.
● An ability to troubleshoot and problem-solve in response to challenges, especially, in an unfamiliar domain.
● Experience with AWS is a plus but not required
** Values & Abilities:*
* ● Excellent written and verbal communication skills and ability to communicate effectively with a variety of different stakeholders from various academic backgrounds.
● Respect for diversity and the importance of interdisciplinary teams.
● Self-starter and innovative thinker and a team-player who can collaborate effectively in a university setting.
● Open-minded, assertive, and professional when collaborating and working within our team and with other groups within Northeastern University, Harvard Medical School, and University of Virginia.
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* Key Responsibilities:

** The Data Science and Machine Learning Engineer will be responsible for a wide variety of data-oriented tasks, including:
● Building data preprocessing pipelines for ML/AI models (Tensor Flow/Pytorch) for multi-omics and healthcare data from to achieve state-of-the-art performance.
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