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We're scaling our machine learning capabilities, and we're looking for a Senior Staff Data Scientist - Machine Learning to take the lead. This role owns the end-to-end ML stack that powers production models across our consumer platform - from the feature infrastructure that fuels every model, to training and deploying models that drive millions in business value.

Being part of our cross-functional Decision Science Team means you'll be at the forefront of driving business performance. You'll partner with marketing managers, product managers, and analysts to translate ambiguous business problems into ML systems that ship, scale, and stay reliable in production.

As the tech lead for ML infrastructure and batch modeling within data science, you'll set the technical direction for how we build features, train models, and operationalize predictions. Our organization has fully embraced agentic development environments, and you will be in the driver's sheet of leveraging this technology to increase efficiency and effectiveness of our ML systems. This is a rare opportunity to own a mature, high-leverage ML stack and shape the next generation of it from a position of strength.

Responsibilities

Own the ML stack end-to-end across feature pipelines, model training, and deployment, with broad influence over the team's ML roadmap.

Set the gold standard for production ML and enable the broader organization with tooling and infrastructure to ensure quality across the team - feature engineering hygiene, training reproducibility, deployment patterns, and post-launch monitoring.

Train, deploy, and maintain batch models that power targeting, retention, and personalization, delivering tens of millions of dollars of business value.

Evolve shared infrastructure (feature engineering, MLOps) that empower the entire organization: improve reliability, reduce time-to-feature for downstream modelers, and ensure features are consistent between training and scoring.

Advise and mentor other data scientists on modeling best practices, code quality, and how to ship models that hold up in production. Embracing agentic modes of development to accelerate your work and the team's work

Partner with marketing, product, and analytics leadership to identify the highest-leverage modeling  opportunities, scope them, and turn predictions into actions.

Establish processes and systems to create scalable ML capabilities rather than one-off models - feature reuse, model templates, automated retraining, and monitoring.

Anticipate future business challenges and design ML methodologies, architectures, and systems to address them.

Qualifications

• At least 7 years of experience building and deploying production machine learning systems, with significant time spent owning models end-to-end (data features training deployment monitoring).
• Demonstrated expertise in batch ML model development - including classification, propensity, and uplift modeling - with a track record of models that have driven measurable business impact in production.
• Strong software engineering fundamentals: experience contributing to and maintaining shared ML libraries, feature stores, or feature engineering frameworks (e.g., featlib, feat-layer, Feast, Tecton, or equivalent).
• Hands-on experience training and deploying models on modern ML platforms (Databricks, Spark MLlib, scikit-learn, XGBoost/LightGBM, PyTorch); familiarity with MLOps patterns (CI/CD for models, feature versioning, drift monitoring).
• A demonstrated ability to navigate ambiguity and deliver results that significantly impact the business.
• Excellent communication skills and the ability to work effectively with both technical and non-technical partners.
• Proficiency in Python, SQL, and PySpark.

Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position will be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.