Principal Data Scientist — Remote ML Leader

Ladders; United States; Remote; USD 180,000 - 190,000; Full time

Benefits offered by this job

Career growth opportunities

Access to cutting-edge AI tools

Collaborative culture

Exposure to large-scale data projects

Job summary

Ladders is seeking a Principal Data Scientist to lead advanced data science initiatives in a remote US-based setting. You will own end-to-end model development, from hypothesis testing and forecasting to deployment and validation, mentoring junior team members and partnering with cross-functional stakeholders.

The role emphasizes building scalable analytical solutions, improving data pipelines, and applying probabilistic graph models and modern ML frameworks to solve complex business questions

Qualifications

• 15–18 years of experience in advanced data science roles.
• Extensive leadership experience in deployment of statistical and ML solutions.
• Expertise in hypothesis testing, regression techniques, and advanced statistics.

Responsibilities

• Design and implement robust statistical models and ML algorithms.
• Lead end-to-end development of data science projects from hypothesis testing to forecasting.
• Collaborate with cross-functional teams to translate business needs into analytical solutions.
• Optimize and automate data pipelines for efficient processing.
• Develop, validate, and maintain probabilistic graph models and advanced statistical frameworks.
• Utilize leading ML frameworks for model building and deployment.
• Mentor and guide junior data scientists on the team.

Skills

Leadership

Statistical methods

Hypothesis testing

Python

PySpark

R

Education

Master's or PhD in Data Science/Statistics/CS

Tools

Probabilistic graph models

ML frameworks

Job description

Ladders is seeking a Principal Data Scientist to lead advanced data science initiatives in a remote US-based setting. You will own end-to-end model development, from hypothesis testing and forecasting to deployment and validation, mentoring junior team members and partnering with cross-functional stakeholders.

The role emphasizes building scalable analytical solutions, improving data pipelines, and applying probabilistic graph models and modern ML frameworks to solve complex business questions