Applied Scientist

Great Value Hiring; United States; On-site; USD 338,000 - 386,000; Full time

Job summary

Great Value Hiring is seeking an Applied Scientist to design and execute statistical analyses on complex datasets and build ML models that drive business objectives. You will work closely with stakeholders to define experimentation frameworks and ensure results are reproducible and well-documented.

The ideal candidate has strong Python experience (Pandas, scikit-learn), a solid background in statistics or applied mathematics, and excellent communication skills to present findings and

Qualifications

• Experience designing and executing statistical analyses on complex datasets.
• Developing and validating ML models for business or research goals.
• Proficiency with Python, Pandas, and data transformations.
• Excellent written and verbal communication; clear technical docs.
• Experience collaborating on cross-functional data science projects.

Responsibilities

• Design and execute statistical analyses to uncover actionable insights.
• Develop and validate ML models for various objectives.
• Collaborate with stakeholders to define experimentation frameworks.
• Document methodology, findings, and recommendations clearly.
• Deliver reproducible code and well-organized analytical reports.

Skills

Statistical analysis

Machine learning

Experiment design

Data modeling

Communication

Education

Advanced degree in data science or statistics

Tools

Python

Pandas

scikit-learn

Job description

Applied Scientist [$245 - $280/hour]

• Design and execute statistical analyses to uncover actionable insights from complex datasets
• Develop and validate machine learning models for various business or research objectives
• Apply data modeling techniques and manage large-scale data transformations using Python and Pandas
• Collaborate with stakeholders to define experimentation frameworks and analyze outcomes
• Document methodology, findings, and recommendations with a focus on clarity and precision
• Review peer contributions and provide high-quality feedback to support knowledge sharing
• Deliver reproducible code and well-organized analytical reports as part of the project deliverables
• Evaluate AI model outputs on statistics, machine learning, experimentation, and quantitative problems
• Create expert-level prompts, datasets, and reference solutions
• Identify flawed methodology, statistical errors, and weak quantitative reasoning
• Provide clear, structured feedback that improves model capability

Scope of Work

• Design and execute statistical analyses to uncover actionable insights from complex datasets
• Develop and validate machine learning models for various business or research objectives
• Apply data modeling techniques and manage large-scale data transformations using Python and Pandas
• Collaborate with stakeholders to define experimentation frameworks and analyze outcomes
• Document methodology, findings, and recommendations with a focus on clarity and precision
• Review peer contributions and provide high-quality feedback to support knowledge sharing
• Deliver reproducible code and well-organized analytical reports as part of the project deliverables
• Evaluate AI model outputs on statistics, machine learning, experimentation, and quantitative problems
• Create expert-level prompts, datasets, and reference solutions
• Identify flawed methodology, statistical errors, and weak quantitative reasoning
• Provide clear, structured feedback that improves model capability

Preferred Qualifications

• At least one year at a top company (tech, finance, or research)
• Expertise in Python, including proficiency with libraries such as Pandas and scikit-learn (sklearn)
• Demonstrated experience in applied statistics, experimental design, and machine learning
• Proven ability to develop, tune, and evaluate machine learning models in practical settings
• Strong data modeling skills and experience managing structured and unstructured data
• Excellent written and verbal communication skills, with a passion for clear technical documentation
• Experience collaborating on open or cross-functional data science projects is a plus
• Advanced academic background or hands-on work in data science, applied mathematics, or related fields