Applied Machine Learning Scientist

Job Description The Enterprise Machine Learning team drives organizational value through scalable ML solutions and data-driven insights, fundamentally changing how business decisions are made. We collaborate closely with stakeholders, applying the latest advances in machine learning and statistical modeling to create highly impactful outcomes. Our commitment is to advance the state of applied science and robust system design to enhance and expand our core business capabilities.

Job Description The Enterprise Machine Learning team drives organizational value through scalable ML solutions and data-driven insights, fundamentally changing how business decisions are made. We collaborate closely with stakeholders, applying the latest advances in machine learning and statistical modeling to create highly impactful outcomes. Our commitment is to advance the state of applied science and robust system design to enhance and expand our core business capabilities.

Role Overview We've spent the past year consolidating and validating our revenue data, most signals now live in one place. The next step is building a customer intelligence layer: systems that dynamically learn which signals drive outcomes, adapt as the business evolves, and surface insights that change how we act.

As an Applied Machine Learning Scientist, you will be the person who makes that happen. You will train models, design experiments, and uncover the patterns that connect customer behavior to revenue outcomes. Then ship those insights as production systems that the business relies on daily. You own problems end-to-end: from formulating the right question, to training and validating models, to deploying them and measuring whether they actually moved the needle, in a closed feedback loop.

You will work alongside a team of AI/ML Engineers, Data Engineers, and Analysts as part of the Enterprise Data & Analytics department. Your focus is the science: understanding what drives outcomes, building models that learn from data, and turning that understanding into systems that work.

Key Responsibilities

Design, train, evaluate, and deploy machine learning models that predict and explain revenue-related outcomes, including churn, expansion, conversion, and customer engagement.

Build and own end-to-end ML systems, from data preparation and experimentation through production deployment, continuous retraining, monitoring, and performance optimization.

Develop intelligence from structured and unstructured data, applying statistical modelling, modern NLP, embeddings, LLMs, and deep learning where they create measurable business value.

Design and run experiments to uncover the relationships between customer behavior, product usage, and business outcomes, translating model outputs into actionable insights.

Partner closely with Product, Engineering, and business stakeholders to identify high-impact opportunities, prioritize ML initiatives, and embed intelligence into decision-making workflows.

Measure and maximize business impact by monitoring model performance, detecting drift, validating outcomes, and continuously iterating based on real-world results and feedback.

This Role Is For You If...

You have 3–5 years of experience in Applied Machine Learning, Data Science, or a related quantitative field, with a track record of building models that deliver measurable business impact.

You bring foundation in machine learning and statistics, with hands‐on experience training models on messy, real‐world data. You're comfortable dealing with label noise, class imbalance, feature leakage, data drift, and designing robust validation strategies.

You are experienced in applying ML to both structured and unstructured data, including modern NLP techniques, embeddings, LLMs, or deep learning where they create meaningful business value.

You have production‐grade engineering skills, including Python, SQL, software engineering best practices, and experience deploying, monitoring, and maintaining ML models.

You have pragmatic, product‐oriented mindset. You get more satisfaction from shipping a simple model that improves a business metric than building a complex one with marginally better offline performance. You think in terms of business outcomes, user behavior, and measurable impact—not model sophistication alone.

You proactively identify high‐impact opportunities, form opinions based on data and user understanding, and continuously iterate on your work.

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