Applied Scientist

About the role

The role is part of a Machine Learning Growth team focused on expanding predictive capabilities across various marketing channels. The position involves developing and improving machine learning models to optimize customer acquisition through digital marketing.

Responsibilities

• Analyze historical model and campaign performance to identify opportunities for improvement.
• Develop, test, and refine machine learning models using statistical and machine learning techniques.
• Design and execute statistically rigorous experiments to measure causal impact and guide business decisions.
• Work with complex datasets to build reusable data pipelines, metrics, and analytical methods that accelerate model development.
• Translate broad business challenges into structured research questions and provide actionable recommendations.
• Partner closely with Machine Learning, Growth, and Marketing Platform Engineering stakeholders to shape both technical approaches and business strategy.
• Expand machine learning applications beyond direct mail into email, lifecycle marketing, digital channels, and other growth opportunities.

Requirements

• Master’s degree in Mathematics, Statistics, Economics, Operations Research, or a related quantitative discipline.
• Demonstrated experience applying statistical and machine learning techniques to data science, modeling, or research problems.
• Strong Python skills for data analysis, data preparation, and machine learning model development.
• Practical experience with causal inference and experimental design, including rigorous evaluation of models and experiments.
• Ability to work across exploratory data analysis, machine learning research, experimentation, and production-oriented modeling.
• Strong analytical and problem-solving skills, with the ability to navigate ambiguous problems and translate business needs into structured research.
• Excellent communication skills and the ability to explain technical findings and recommendations to both technical and business stakeholders.
• A PhD in Mathematics, Statistics, Economics, Operations Research, or a related field is a plus.
• Knowledge of causal machine learning methods and experience with marketing, customer acquisition, lifecycle, or growth applications are advantageous.
• Experience scaling production machine learning models and collaborating across engineering and business teams is preferred.

Benefits

• Competitive compensation: U.S. remote base salary range of $141,500–$196,000 USD, depending on location, skills, experience, and education.
• Performance and equity: Target bonus opportunities and annual equity grants that vest quarterly.
• Retirement benefits: 401(k) with a company match of $2 for every $1 contributed, up to $15,000 annually.
• Health coverage: Comprehensive medical, dental, vision, and wellness benefits, with Health Savings Account contributions for eligible U.S. plans.
• Financial benefits: Employee Stock Purchase Plan with discounted stock options for eligible U.S. employees.
• Income protection: Life insurance and disability coverage.
• Family support: Benefits supporting fertility, parenthood, caregiving, and other family needs.
• Wellbeing: Employee Assistance Program, mental health resources, and an annual wellness allowance.
• Professional productivity: Annual productivity allowance to help cover tools and resources needed to work effectively.
• Remote-first flexibility: Work remotely across the U.S., with regular team onsites and in-person collaboration, typically once or twice per quarter for 2–4 days.
• Community and connection: Team events, company-wide gatherings, and employee resource groups.
• Office perks: Catered lunches and stocked kitchens at applicable office locations.