Senior Machine Learning Engineer, Economist
Build and deploy machine learning systems that apply economic theory, econometrics, and causal inference to marketplace problems. The role requires advanced training in economics, strong Python and data skills, and production ML experience for senior-level hires.
About the job
Responsibilities
- Design, develop, and deploy machine learning solutions for economic challenges in a complex marketplace.
- Collaborate with product managers, data scientists, and engineers to understand business needs and create impactful solutions.
- Refine and advance algorithms and models to improve operational efficiency.
- Share learnings, best practices, and research across Economics team domains.
Requirements
- Master's or PhD in Economics or a closely related field.
- Knowledge of economic theory, applied econometrics, and business applications.
- Experience applying causal inference methodologies to observational and experimental datasets.
- Understanding of machine learning algorithms and techniques.
- Strong Python programming skills.
- Fluency with SQL and Pandas for data manipulation.
- Experience with machine learning tools such as scikit-learn and XGBoost.
- Strong verbal and written communication skills.
- Self-motivation and ownership.
Additional Senior-Level Requirements
- 1–3 years of industry experience in a similar position.
- Experience deploying machine learning models to production.
- Experience with cloud computing and ML infrastructure.
Preferred Qualifications
- PhD in Economics or a closely related field focused on data-intensive problems.
- Related internship experience.
- Experience with large language models and generative AI.
- Experience with uplift modeling, contextual bandits, or heterogeneous treatment effect estimation.
Compensation
- ATS-listed base salary range: $173,000–$218,500 USD, depending on location.
- The role may also include a new-hire equity grant and annual refresh grants.
- Benefits are provided according to work location.
Skills
Python, SQL, pandas, scikit-learn, Xgboost, Causal Inference, Econometrics, Machine Learning, Cloud Computing, ML Infrastructure, LLMs, Generative AI, Uplift Modeling, Contextual Bandits, Heterogeneous Treatment Effects
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