Senior Data Scientist
New
I
Intertape Polymer GroupMaterials science
Source API remote eligibility restrictions: United StatesFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 10+ years of professional experience in data science, applied analytics, or scientific computing
- Required Skills
- PythonSQLMachine LearningData modelingR
Requirements
- Hold a bachelor’s degree in Data Science, Computer Science, Statistics, Materials Science, Chemical Engineering, or a related field; a master’s degree or PhD is preferred.
- Have 10+ years of professional experience in data science, applied analytics, or scientific computing.
- Demonstrate strong proficiency in Python and/or R for data analysis and modeling.
- Have solid experience with SQL and structured and semi-structured datasets.
- Have a strong foundation in statistics, experimental design, and multivariate analysis.
- Have demonstrated experience applying machine learning to real-world, noisy scientific or experimental data.
- Work effectively in a cross-functional R&D environment and communicate complex analyses as actionable insights.
- Familiarity with bio-polymers, sustainable materials, or polymer processing is preferred.
- Experience with DOE software, laboratory data management systems (LIMS), or scientific databases is preferred.
- Experience deploying models for R&D decision-making or manufacturing scale-up is preferred.
- Familiarity with cloud platforms such as AWS or Azure and data science lifecycle tools is preferred.
- Prior experience mentoring or leading technical projects is preferred.
Responsibilities
- Partner with polymer scientists, chemists, and engineers on bio-polymer research and development using data-driven methods.
- Analyze and model experimental, formulation, and process data to identify structure–property–process relationships.
- Develop predictive models for material performance and property optimization, formulation design and screening, and scale-up and process optimization.
- Design and analyze experiments (DOE) to maximize learning efficiency and reduce development timelines.
- Build and maintain reproducible data workflows for R&D data ingestion, cleaning, and analysis.
- Apply machine learning techniques, including regression, classification, clustering, and time-series modeling, to scientific datasets.
- Collaborate with data engineering and IT teams to enable scalable data infrastructure for R&D.
- Communicate insights, tradeoffs, and recommendations to technical and non-technical stakeholders.
- Contribute to data dictionaries, process flow diagrams, and R&D data science best practices; mentor junior data scientists or technical staff.
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