Economist, MLE II

3 weeks ago


Vancouver, Canada Instacart Full time
OVERVIEW

We are looking for economists and machine learning engineers to join our fast-moving team. The Economics team at Instacart works on a range of interesting and challenging problems, from aligning the incentives in our multi-sided marketplace to analyzing the role of prices and product placement in our customers' decision-making.

There is tremendous opportunity in front of us, and joining now gives you a chance to grow your career and interests as we succeed.

The ideal candidate for this role will bring a combination of experience in both economics and machine learning. This might be a recent economics PhD graduate with relevant research experience, typically in computationally intensive empirical topics, as well as some exposure to machine learning coursework and applications. We are also interested in candidates with industry experience in the creation, deployment, and maintenance of machine learning models at scale, especially if they have some background or experience in economics or an economics-adjacent field.

ABOUT THE TEAM

The Economics team at Instacart works on a range of interesting and challenging problems, from aligning the incentives in our multi-sided marketplace to analyzing the role of prices and product placement in our customers' decision-making. Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference.

ABOUT THE JOB
  • You will help design and build end-to-end machine learning solutions
  • You will be working in small and cross-functional product teams, with great opportunities for growth and ownership of projects
  • You will be an active member of an internal community, including economists, data scientists, operations research scientists and machine learning engineers, sharing learnings, best practices and research across many domains
  • You will develop high impact solutions to support Instacart's ambitious growth plans
  • You will work closely with engineers, product managers, other teams, and both internal and external stakeholders, owning a large part of the process from problem understanding to recommending a solution and testing it in controlled experiments
  • You will have the freedom to suggest and drive organization-wide initiatives

ABOUT YOU

MINIMUM QUALIFICATIONS

  • Graduate-level research experience on data-intense problems or 1+ years of industry experience in machine learning or economics-focused roles
  • A blend of economic theory, applied econometrics, and business skills that let you jump into a fast-paced environment and contribute from day one
  • Expertise in causal inference with observational and experimental data
  • Strong engineering skills with expertise in R or Python and fluency in data manipulation (SQL, Pandas) and machine learning (scikit-learn, XGBoost, Keras/Tensorflow) tools
  • An ability to identify and prioritize high-impact problems and deliver solutions that provide reasonable trade-offs between urgency and quality
  • Willing and able to travel internationally based on job requirements
  • Self-motivation and a strong sense of ownership

PREFERRED QUALIFICATIONS

  • PhD in Economics with focus on data-intense problems or 3-5 years of industry experience in machine learning or economics-focused roles
  • A blend of economic theory, applied econometrics, and business skills that let you jump into a fast-paced environment and contribute from day one
  • Expertise in causal inference with observational and experimental data
  • Strong engineering skills with expertise in R or Python and fluency in data manipulation (SQL, Pandas) and machine learning (scikit-learn, XGBoost, Keras/Tensorflow) tools
  • An ability to identify and prioritize high-impact problems and deliver solutions that provide reasonable trade-offs between urgency and quality
  • Willing and able to travel internationally based on job requirements
  • Self-motivation and a strong sense of ownership
  • Experience with training large-scale models and model deployment on cloud services (Docker, AWS/GCP/Azure)

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