Senior Machine Learning Engineer, Economist

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Machine Learning Engineer, Economist based in the United States.

This role combines economic research, causal inference, and advanced machine learning to solve complex problems within a large-scale, multi-sided marketplace. You will design and deploy sophisticated ML systems while applying rigorous economic thinking to questions involving pricing, incentives, customer behavior, and marketplace dynamics. Working as part of a highly collaborative horizontal team, you will partner with product managers, data scientists, and engineers across multiple functions. The position offers broad exposure to challenging problems including matching and logistics, advertising, uplift modeling, long-term value, and causal inference. You will have the opportunity to contribute both as a technical builder and as an economist, helping advance models, algorithms, and engineering practices. This is an environment well suited to technically minded economists who enjoy research-driven problem solving, ownership, and fast-paced experimentation.

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Accountabilities:

• 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.

• For senior-level hires, 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.

Requirements:

• Master's or PhD degree in Economics or a closely related quantitative field, with a PhD and data-intensive research background preferred.

• 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, with the ability to explain technical and economic concepts clearly to both technical and non-technical stakeholders.

• Strong ownership, self-motivation, curiosity, and ability to operate effectively in a fast-moving, collaborative environment.

• For senior-level candidates, 1–3 years of relevant industry experience and demonstrated experience deploying machine learning models into production environments.

• For senior-level candidates, experience with cloud computing and machine learning infrastructure is preferred.

• Experience with large language models and generative AI, whether for algorithm development or as part of day-to-day technical workflows, is a plus.

• Familiarity with uplift modeling, contextual bandits, heterogeneous treatment effects, or related advanced causal ML methodologies is highly valued.

• Relevant internship experience in economics, machine learning, data science, or similar technical roles is advantageous.

Benefits:

• Base salary range of $180,000–$190,000 CAD for eligible Canadian-based candidates.

• Eligibility for a new-hire equity grant and annual equity refresh grants.

• Highly competitive compensation and benefits aligned with the employee's permanent work location.

• Flexible, remote-first work model with the ability to work from home, an office, or another suitable location.

• Flexibility supported by regular opportunities for in-person connection and team events.

• Inclusive and collaborative environment focused on technical innovation, learning, and professional growth.

• Benefits and compensation may vary by location and individual experience.

\nHow 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!

Why Apply Through Jobgether?

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