Staff Applied Scientist

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

For our client, we are seeking a Staff Applied Scientist to join the team of a leader in the Finance & Insurance space.

This role will focus on advancing machine learning models that improve credit decisioning and underwriting performance in a highly data-driven environment. You will lead rigorous experimentation, analyze model behavior, and turn research insights into practical methodologies that strengthen decision quality. Partnering closely with Engineering, you will help bring new approaches from concept through deployment while supporting reliable, scalable technical outcomes.

Location: Remote - US based candidates only, no visa sponsorship available

Compensation: $199,500 – $276,000 annually

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

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

Benefits

• Competitive compensation with base pay, bonuses, and equity grants
• 401(k) with company match to support retirement planning
• Comprehensive health coverage including medical, dental, and vision
• Paid family and parental leave for caregiving support
• Employee Assistance Program for mental health and life resources

Our client is an equal opportunity employer. We encourage you to apply even if you don’t meet every qualification—your background could be exactly what this team needs.