Remote Data Scientist – Tools, ML & Research Innovation

Ladders; United States; Remote; USD 114,000 - 140,000; Full time

Benefits offered by this job

Remote work within US

Total Rewards Program

Professional development

Collaborative environment

Job summary

Ladders is seeking a Data Scientist for Project Success and Innovation, fully remote within the United States. You will support research and evaluation by building shared analytic tools, prototyping ML applications, and preparing training materials for diverse audiences.

You will work with large datasets, develop user-friendly tools, and help teams adopt new workflows, translating insights into accessible reports for non-technical stakeholders.

Qualifications

• Master’s or doctoral degree in a quantitative field such as statistics, data science, or economics.
• Experience with complex survey data and large-scale datasets.
• Familiarity with machine learning and AI methods, particularly large language models.
• Interest in developing user-friendly tools and supporting others in their use.
• Proficiency in R and/or Python, with working knowledge of Stata.

Responsibilities

• Build and maintain shared analytic tools for research teams
• Write documentation and training materials for diverse technical audiences
• Support project teams in adopting new tools and workflows
• Prototype and test new tools, including machine learning applications
• Conduct quantitative analyses for research and evaluation projects
• Manage and quality‑assure complex datasets from multiple sources
• Draft analytic sections of reports and translate findings for non-technical audiences

Skills

Masters/PhD in quantitative field

Survey data / large-scale datasets

ML/AI methods, LLMs

Tool development / user-friendly tools

R / Python, Stata knowledge

Education

Master’s or PhD in statistics / data science / economics

Tools

R

Python

Stata

Job description

Ladders is seeking a Data Scientist for Project Success and Innovation, fully remote within the United States. You will support research and evaluation by building shared analytic tools, prototyping ML applications, and preparing training materials for diverse audiences.

You will work with large datasets, develop user-friendly tools, and help teams adopt new workflows, translating insights into accessible reports for non-technical stakeholders.