Staff Machine Learning Engineer - Pricing & Revenue

About the Role

As a Staff Machine Learning Engineer specializing in Pricing and Revenue, you will lead the technical direction and comprehensive ownership of ML initiatives aimed at pricing and revenue optimization. Collaborating closely with Product and Engineering teams, you will ensure models not only appear sound but deliver reliable, measurable business results. Your leadership will stem from expertise and ownership rather than formal personnel management.

Key Responsibilities

• Manage the complete lifecycle of pricing and revenue machine learning solutions, from hypothesis formulation through deployment and rigorous evaluation focused on tangible business benefits.
• Develop, refine, and optimize forecasting and pricing algorithms, selecting the most efficient and stable approaches to achieve objectives.
• Handle diverse data inputs like time series and demand signals, guaranteeing that features and target variables are cleanly defined without data leakage.
• Design and implement a robust experimentation framework including holdouts, A/B testing, and control metrics to inform rollout decisions.
• Establish engineering standards for backtesting, reproducibility, and version control, ensuring high-quality, production-grade machine learning workflows beyond exploratory notebooks.
• Maintain operational reliability by implementing smart monitoring, drift detection, and pragmatic retraining strategies.
• Automate critical processes such as backtesting and monitoring to enhance efficiency and output consistency.
• Construct foundational data models in cloud data warehouses (e.g., Snowflake) using tools like dbt to support reliable metrics and feature sets.
• Create and standardize business performance dashboards (e.g., Metabase) to maintain transparent and accurate KPIs.
• Engage with stakeholders to prioritize feature requirements and convert them into impactful ML solutions, focusing on effectiveness over volume.

Candidate Profile

• At least five years of professional experience in machine learning engineering, data science, or analytics, demonstrating significant accomplishments.
• Proven track record of successful ML applications in pricing, revenue management, forecasting, or related financial domains.
• Expertise in evaluation methodologies, including identification of bias and data leakage, and mastery of robust metrics and guardrails.
• Proficiency in Python and SQL with a focus on production-quality, testable, version-controlled, and reproducible code.
• A pragmatic, startup-oriented mindset embracing the 80/20 principle and autonomous ownership.
• Fluent communication skills in English.

Preferred Qualifications

• Domain-specific experience in dynamic pricing or revenue management sectors such as travel, mobility, or e-commerce.
• Understanding of demand fluctuations influenced by seasonality, events, and lead times.
• Familiarity with modern analytics engineering toolchains including dbt, Snowflake, and Metabase to develop clean and scalable data foundations.