Applied ML Scientist

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.

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 ML 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.

• Train, evaluate, and deploy models that predict and explain revenue-related outcomes (churn, expansion, conversion, engagement)

• Design and run experiments to establish relationships between customer signals and business results

• Build production ML pipelines that learn continuously: ingest new data, retrain, validate, and serve predictions at scale

• Work with large volumes of both structured data (usage metrics, revenue events, account attributes) and unstructured data (support tickets, conversations, product feedback)

• Use LLMs and deep learning where they're the right tool (embeddings, fine-tuning, text feature extraction)

• Define and track model performance in production: monitor drift, measure business impact, and iterate when models degrade

• Own your models end-to-end from prototype through production deployment, monitoring, and maintenance

• Partner with product and engineering to embed intelligence where users make decisions.

• Translate model outputs into actionable insights for sales, success, and product teams: make the intelligence layer useful, not just accurate

• Collaborate with stakeholders to identify high-leverage questions and prioritize modeling work based on expected business impact

• You get more satisfaction from a simple model that ships and moves a metric than a complex one that scores well on a holdout set

• You've trained models on real-world messy data and dealt with the unglamorous parts: label noise, class imbalance, feature leakage, data drift

• You write production Python — tests, type hints, clean abstractions — not just notebooks with df_final_v3

• You think about problems in terms of "what will someone do differently because of this?" rather than "what's the most sophisticated technique?"

• You care that users actually change behavior because of your work: you're not done when the model is accurate, you're done when someone acts differently

• You form opinions about what to build next based on data and user understanding, not just what's assigned to you

• 3–5 years' experience in applied machine learning, data science, or a related field

• BA/BS in Computer Science, Statistics, Mathematics, or related quantitative discipline

• Advanced degrees welcome but not required

• Strong foundations in statistical modeling and machine learning: regression, classification, survival analysis, causal inference, uplift modeling, or similar

• Experience training models on real-world datasets: feature engineering, validation strategy, handling messy data at scale

• Comfort working with unstructured data (text, conversations) using embeddings, fine-tuning, or learned representations: you understand LLMs as modeling tools, not just API endpoints

• Strong Python programming skills: you write production-grade code with tests, not just scripts

• Strong SQL skills and experience with cloud data warehouses (Snowflake preferred)

• Experience deploying and monitoring models in production (batch or real-time)

• Nice-to-have: Experience with experiment design, A/B testing, and causal inference

• Nice-to-have: Experience with orchestration tools (Airflow, dbt, or similar)

• Nice-to-have: Experience with AI-assisted development workflows (Claude Code, Cursor, Copilot, or similar)

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