Data Scientist: Turn Questions into Evidence

Peregrine Advisors LLC; Washington (District of Columbia); On-site; USD 120,000 - 180,000; Full time

Job summary

Peregrine Advisors LLC is seeking a Data Scientist to transform complex data into evidence. You will work with stakeholders to define metrics, assess data viability, select ML methods, and explain results for informed decisions.

The role spans exploratory analysis to model development, validation, interpretation, and communication, with emphasis on bias, uncertainty, and reproducibility across projects.

Qualifications

• Working foundation in statistics, probability, research design, ML, optimization or a quantitative discipline.
• Ability to prepare, explore, and analyze data using Python, R, or SQL with attention to quality and provenance.
• Experience selecting methods, validating assumptions, comparing models, and interpreting results in context.
• Reproducible practice through documented code, version control, peer review, and clear analytical records.
• Ability to explain uncertainty, bias, limitations, and appropriate use without hiding the central finding.

Responsibilities

• Apply statistical or ML methods to real-world questions defined with stakeholders.
• Build, validate, and interpret forecasting, classification, and other models.
• Design experiments and evaluation frameworks for decision-relevant insights.
• Produce reproducible analytical pipelines, notebooks, and model cards.
• Communicate results and uncertainties clearly to technical and nontechnical audiences.

Skills

Statistics

Machine learning

Data analysis

Python

R

SQL

Education

Quantitative discipline

Tools

Python

R

SQL

Job description

Peregrine Advisors LLC is seeking a Data Scientist to transform complex data into evidence. You will work with stakeholders to define metrics, assess data viability, select ML methods, and explain results for informed decisions.

The role spans exploratory analysis to model development, validation, interpretation, and communication, with emphasis on bias, uncertainty, and reproducibility across projects.