Remote Staff Applied Scientist - ML for Credit Underwriting

Ladders; United States; Remote; USD 200,000 - 276,000; Full time

Benefits offered by this job

Equity grants

401(k) with company match

Health coverage

Parental leave

Employee Assistance Program

Job summary

Ladders is seeking a Staff Applied Scientist to advance ML models for credit decisioning and underwriting in a data-driven environment. You will lead experiments, analyze model behavior, and translate insights into deployable methods that improve decision quality, in collaboration with the Engineering team.

The role requires a graduate degree in a quantitative field and 2+ years of ML experience, with strong Python skills and a focus on rigorous experimentation.

Qualifications

• Graduate degree in a quantitative field such as mathematics, statistics, or computer science.
• 2+ years of experience in machine learning or statistical modeling.
• Proficiency in Python for model development and evaluation.
• Strong foundation in probability, statistics, and machine learning methods.
• Experience in designing experiments to assess model performance.

Responsibilities

• Research and implement machine learning techniques to enhance underwriting models.
• Design and evaluate model improvements through rigorous experimentation.
• Analyze model performance to ensure reliable decision-making.
• Collaborate with Engineering teams for technical reviews and deployment.
• Translate research findings into actionable recommendations and methodologies.

Skills

Python

Experiment design

Statistics

ML fundamentals

Education

Graduate degree in mathematics, statistics, or computer science

Job description

Ladders is seeking a Staff Applied Scientist to advance ML models for credit decisioning and underwriting in a data-driven environment. You will lead experiments, analyze model behavior, and translate insights into deployable methods that improve decision quality, in collaboration with the Engineering team.

The role requires a graduate degree in a quantitative field and 2+ years of ML experience, with strong Python skills and a focus on rigorous experimentation.