Applied Scientist

Accountabilities:

• • Analyze historical model and campaign performance to identify opportunities to improve predictive effectiveness and marketing outcomes.
  • Develop, test, and refine machine learning models, including researching new features, modeling approaches, and architectures.
  • Design statistically rigorous experiments and evaluations to measure causal impact, assess model performance, and guide business decisions.
  • Work with complex and imperfect datasets, building reusable data pipelines, metrics, and analytical methods that accelerate model development.
  • Translate broad business challenges into structured research questions, analytical approaches, intermediate milestones, and production-ready solutions.
  • Partner closely with Machine Learning, Growth, and Marketing Platform Engineering stakeholders throughout research, experimentation, development, and deployment.
  • Expand machine learning applications beyond direct mail into email, lifecycle marketing, digital channels, and other emerging growth opportunities.
  • Interpret complex experimental and modeling results and translate findings into clear, actionable recommendations.

  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.
  • Time off: Paid time off, sick leave, company holidays, and paid family and parental leave.
  • 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.
  • Location note: The role is available to candidates based in the United States; the broader compensation framework also supports Canada, excluding Quebec.