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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