Job Type: Contract
Contract Length: 6 months
Pay Range: $40-48/hr
Start Date: ASAP
Location: Hybrid - Westbrook, ME
About the Opportunity:
Our client, a leader in the healthcare diagnostics industry, is looking for an entry-level Jr. Data Scientist to join their Machine Intelligence team in R&D for a 6-month engagement. This project involves developing machine learning solutions for hematology analyzers, specifically focusing on classification and clustering problems on tabular data for edge hardware deployment. This is an excellent opportunity for a curious, adaptable data scientist to build foundational skills in applied machine learning under the guidance of senior experts.
Key Responsibilities & Deliverables:
This role is focused on the successful completion of specific tasks and deliverables. Your responsibilities will include:
- Developing classification and clustering models on tabular data to support hematology analyzer capabilities.
- Contributing to model development, evaluation, and iteration under the guidance of a senior data scientist.
- Partnering with senior team members to understand requirements, explore data, and validate model performance.
- Documenting work clearly so it can be reviewed, reproduced, and built upon by the team.
- Deploying solutions to edge hardware.
We are looking for someone with a proven track record of successful contract engagements. The ideal candidate will have:
- 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.
- Bachelor's degree in a quantitative field (statistics, computer science, math, engineering, or related); advanced degree a plus.
- A growth mindset, curiosity about the "why" behind data, and the ability to communicate analyses clearly to the team.
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