Staff Machine Learning Engineer (Forecasting & Optimization) Job at Fetch, Chicago, IL

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  • Fetch
  • Chicago, IL

Job Description

Role Overview: As a Staff Machine Learning Engineer focusing on forecasting and optimization, you will design models to forecast offer performance and optimize offer structures. You will use cutting-edge machine learning techniques to improve how Fetch designs offers to maximize redemption rates, user satisfaction, and overall campaign success. Your work will have a direct impact on how Fetch partners with brands to drive ROI.

Responsibilities:

  • Develop and implement machine learning models to forecast offer performance and predict key metrics such as redemption rates, user engagement, and sales uplift.
  • Build optimization algorithms to help design offer structures that maximize both user value and business outcomes.
  • Collaborate with business and product teams to identify optimization opportunities and create data-driven strategies for offer design.
  • Analyze the effectiveness of current offers and make recommendations to improve future performance.
  • Use experimentation and simulation techniques to validate forecast models and optimization strategies.
  • Mentor and provide technical guidance to junior engineers and data scientists on the team.

Requirements:

  • 7+ years of experience in machine learning, focusing on forecasting, optimization, or a similar field.
  • Proven experience with machine learning models for time-series forecasting, predictive analytics, and optimization.
  • Strong programming skills in Python, R, or other relevant languages.
  • Expertise in optimization techniques, including linear programming, convex optimization, etc.
  • Experience working with large-scale data and building robust data pipelines.
  • Excellent problem-solving skills and the ability to turn complex business requirements into technical solutions.

Nice to Have:

  • Experience in retail, consumer goods, or loyalty programs.
  • Familiarity with model calibration, causal inference, or reinforcement learning.
  • Experience working in fast-paced, agile tech environments.
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