Jr. Data Scientist 00021

Job Description

Hybrid -Westbrook, ME

Job Description

The Machine Intelligence team in R&D is looking for an entry-level Data Scientist to develop machine learning solutions for the hematology analyzers. In this role, you will work on classification and clustering problems on tabular data, with solutions deployed on edge hardware in our analyzer platforms. You will work under the supervision of a senior data scientist who will guide your technical development and project execution. We are looking for a curious, adaptable team player eager to build foundational skills in applied machine learning.

What you can expect

Develop classification and clustering models on tabular data to support hematology analyzer capabilities
Contribute to model development, evaluation, and iteration under the guidance of a senior data scientist
Partner with senior team members to understand requirements, explore data, and validate model performance
Document your work clearly so it can be reviewed, reproduced, and built upon by the team
Deploy your solutions to edge hardware

What you need to succeed

0-2 years of experience applying machine learning to real-world problems (internships, research, and coursework projects count)
Strong working knowledge of Python and common data science libraries (pandas, scikit-learn, NumPy)
Solid foundation in statistics, machine learning, and algorithms
Demonstrated understanding of classification and clustering methods for tabular data, including when to apply which approach and how to evaluate results
Curiosity about the data and the underlying generating processes - a habit of asking "why" before reaching for a model
A growth mindset and willingness to learn from more senior team members
Ability to communicate analyses and results clearly to your immediate team
Bachelor's degree in a quantitative field (statistics, computer science, math, engineering, or related); advanced degree a plus

Nice to have

Exposure to deploying ML models on resource-constrained or edge hardware
Familiarity with model optimization techniques (quantization, ONNX, TFLite)
Experience with version control (Git) and collaborative software development practices
Experience modeling data for medical, diagnostic or life sciences applications