Data Scientist

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

Qualifications

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