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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