Applied Scientist

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an Applied Scientist based in the United States.

As an Applied Scientist, you will develop and improve machine learning models that optimize customer acquisition across digital marketing channels.
You’ll combine statistical modeling, causal inference, experimentation, and business analysis to solve complex growth problems.
The role sits within a Machine Learning Growth team focused on expanding predictive capabilities across direct mail, email, digital, and lifecycle marketing.
You’ll work with large, messy datasets and turn them into reliable models, experiments, metrics, and actionable insights.
Your work will directly influence marketing effectiveness, prospect selection, and customer acquisition outcomes.
The environment is highly collaborative, research-oriented, and production-focused, with opportunities to shape both technical approaches and business strategy.
This is a digital-first role offering remote flexibility across the U.S., with periodic in-person team collaboration.

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.

How Jobgether works:

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

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We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.