Data Scientist
Intone Networks
United States
1 day ago
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Role details
Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Job source
Tech stack
Data Analysis
Computer Vision
Computer Simulation
Computer Programming
Data Files
Hyperspectral Imaging
Python (Programming Language)
Machine Learning
Standard Sql
Scientific Computating
Uncertainty Quantification (UQ)
Model Validation
Job description
This is a contract position for a Data Scientist based remotely in the US, with an expected duration of 6+ months. The role focuses on developing rigorous quantitative solutions to complex scientific and business problems using statistical reasoning, mathematical modeling, and computational methods.
Responsibilities
- Take ambiguous scientific or business problems and determine the appropriate mathematical or statistical approach
- Work through imperfect real-world datasets to develop defensible quantitative solutions
- Develop and validate statistical and machine-learning models with clear understanding of the underlying reasoning and mathematics
- Assess data sufficiency and appropriateness for problem-solving
- Validate conclusions, models, and results through rigorous experimental design and appropriate performance metrics
- Account for uncertainty, limitations, assumptions, and data quality issues in analytical approaches
- Translate complex technical concepts clearly for both technical and non-technical stakeholders
- Independently investigate unfamiliar technical problems and select appropriate methodologies
Requirements
Data Science, Mathematical Modeling, Mathematics, Data Sets, Machine Learning, Data Analysis, Problem Solving Skills, Performance Metrics, Experiment Design, Model Validation, Required
- Strong proficiency with Python and SQL
- Strong foundation in statistics, probability, and applied mathematics
- Experience developing and validating statistical and machine-learning models
- Experience working with messy, sparse, imbalanced, or otherwise imperfect real-world datasets
- Strong understanding of experimental design, model validation, uncertainty quantification, and appropriate performance metrics
- Ability to translate ambiguous scientific or business problems into testable quantitative approaches
- Experience working with multiple types of data, such as tabular, time-series, spatial, sensor, spectral, or image data
- Ability to independently investigate unfamiliar technical problems and determine the appropriate mathematical or statistical methodology
- Strong programming and general computer science fundamentals
- Ability to communicate complex technical concepts effectively with both technical and non-technical stakeholders
Preferred
- Geospatial analysis
- Environmental or weather data
- Time-series modeling
- Computer vision
- Hyperspectral or spectral data
- Risk modeling
- Optimization
- Scientific computing
- Production engineering
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