Applied Scientist

Ladders; United States; Remote; USD 142,000 - 196,000; Full time

Benefits offered by this job

Bonuses & equity

401(k) match

Stock purchase plan

Health coverage

Parental leave

Wellness allowance

Job summary

Ladders is seeking an Applied Scientist to join our leadership in the Finance & Insurance space, focusing on building ML and statistical solutions that improve how customer offers are designed and selected. You will partner with cross-functional stakeholders to translate research into production-ready outcomes that support better decision-making and business performance in a data-driven environment.

The work blends causal inference, experimentation, and applied modeling, with an emphasis on

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 causal inference methods and experimentation.
• Expertise in applied statistics and experimental design.
• Production-quality coding experience in Python.

Responsibilities

• Develop causal machine-learning models to optimize customer offers
• Analyze complex data and design experiments for model evaluation
• Measure the impact of offer changes through rigorous statistical experiments
• Translate research findings into actionable recommendations
• Build production-ready models for offer selection
• Collaborate with cross-functional teams on strategy and implementation

Skills

Machine learning

Statistical modeling

Causal inference

Experiment design

Data analysis

Education

Graduate degree in Mathematics/Statistics/CS

Tools

Python

Job description

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

This role will focus on building and evaluating machine learning and statistical solutions that improve how customer offers are designed and selected. You will partner closely with cross-functional stakeholders to translate research into practical, production-ready outcomes that support better decision-making and business performance. The work blends causal inference, experimentation, and applied modeling in a highly data-driven environment. Strong collaboration and the ability to turn complex analysis into clear recommendations will be essential.

Location: Remote

Compensation: $141,500 - $196,000 annually

Responsibilities

• Develop causal machine-learning models to optimize customer offers
• Analyze complex data and design experiments for model evaluation
• Measure the impact of offer changes through rigorous statistical experiments
• Translate research findings into actionable recommendations
• Build production-ready models for offer selection
• Collaborate with cross-functional teams on strategy and implementation

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 causal inference methods and experimentation
• Expertise in applied statistics and experimental design
• Production-quality coding experience in Python

Benefits

• Competitive compensation with bonuses and equity grants
• 401(k) with company match for retirement savings
• Employee stock purchase plan with discounts
• Comprehensive health coverage including medical, dental, and vision
• Paid family and parental leave for caregiving support
• Annual wellness and productivity allowances for personal development

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.