Applied Scientist
J
JobgetherMachine Learning
Available to candidates based in the United States; the broader compensation framework also supports Canada, excluding QuebecFull-TimeMiddle
SalaryU.S. remote base salary range of $141,500–$196,000 USD
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Job Details
- Required Skills
- PythonData AnalysisMachine Learning
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.
Responsibilities
- 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.
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