What are the responsibilities and job description for the Data Scientist -- CHADC5832849 position at Compunnel Inc.?
6 Months Contract
Onsite in Westbrook - Hybrid (Tuesdays 1 other day)
Seniority:
Entry level DS. Will be partnered with more senior members. Fresh Grad is OK - ideally some real world experience
Project:
ProCyte One hematology analyzer
Responsibilities:
Will be working on classification and cluster problems
All data is tabular data about cells
Digging into spreadsheets
Will be working with people familiar with platform.
Needed:
Python / Pandas or other DS library
Stats / ML/ Algos
TensorFlow - not a MUST have
Growth mindset
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