Senior Machine Learning Engineer, Economist
New
J
JobgetherMachine Learning, Economics
Based in the United StatesFull-TimeSenior
SalaryBase salary range of $180,000–$190,000 CAD for eligible Canadian-based candidates.
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Job Details
- Experience
- 1–3 years of relevant industry experience
- Required Skills
- PythonSQLCloud ComputingMachine LearningPandasscikit-learn
Requirements
- Master's or PhD degree in Economics or a closely related quantitative field.
- Strong combination of economic theory, applied econometrics, business understanding, and quantitative problem-solving skills.
- Demonstrated experience applying causal inference methodologies to observational and experimental datasets.
- Solid understanding of machine learning algorithms, modeling techniques, and practical applications.
- Strong programming skills in Python, with fluency in SQL, Pandas, and common machine learning frameworks such as scikit-learn and XGBoost.
- Excellent written and verbal communication skills.
- Strong ownership, self-motivation, curiosity, and ability to operate effectively in a fast-moving, collaborative environment.
- 1–3 years of relevant industry experience and demonstrated experience deploying machine learning models into production environments.
- Experience with cloud computing and machine learning infrastructure.
Responsibilities
- Design, develop, test, and deploy machine learning solutions that address complex economic and marketplace challenges at scale.
- Apply economic theory, econometric methods, causal inference, and machine learning techniques to both observational and experimental data.
- Collaborate closely with product managers, data scientists, software engineers, and other cross-functional partners to understand business problems and translate them into high-impact technical solutions.
- Develop and continuously improve algorithms and models to increase operational efficiency, improve decision-making, and generate measurable business impact.
- Contribute to projects across areas such as marketplace matching and logistics, online advertising, customer decision-making, uplift and long-term value modeling, and causal inference.
- Help advance the team's technical capabilities by sharing research findings, engineering practices, modeling approaches, and lessons learned across different problem areas.
- Productionize machine learning models and contribute to scalable ML infrastructure and cloud-based deployment practices.
- Take ownership of projects from problem definition through implementation and deployment, maintaining a strong focus on technical quality and practical outcomes.
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