Required Staff Data Scientist
Job Description
As a Staff Data Scientist, youll play a highly embedded and influential role within our Data Science organisation, partnering closely with Development and Product teams across multiple products within ITOM. Rather than simply responding to predefined requirements, youll bring your own creativity, domain expertise, and ideas to identify opportunities, challenge assumptions, and solve complex problems using real customer data.
What you get to do in this role:
Lead the development and evolution of advanced data science and machine learning capabilities, including noise reduction, anomaly detection, root cause analysis, and new AI-powered product features.
Own complex data science problems end-to-end, from problem definition and ideation through experimentation, model development, evaluation, productionization, and deployment.
Develop advanced algorithms and models using techniques such as machine learning, deep learning, NLP, anomaly detection, and statistical modeling.
Partner closely with Product, Engineering, Quality, and Architects to translate product requirements into scalable, reliable ML solutions.
Drive technical direction and mentor other data scientists, contributing to best practices in modeling, experimentation, evaluation, and ML engineering.
Stay current with advances in AI/ML and identify opportunities to apply emerging technologies to our products.

We are seeking a visionary and hands-on Staff AI Scientist to help shape the next generation of foundational language models and agentic systems that empower small and medium-sized businesses to thrive. In this role you will define, design, and build groundbreaking AI solutions that understand, reason, and act across complex financial and business contexts.

You will collaborate closely with cross-functional experts across AI research, data science, engineering, and product to create intelligent agents that help SMBs automate financial tasks, make smarter decisions, and operate more efficiently. Your work will combine scientific rigor with visionary execution, balancing theoretical innovation with large-scale practical impact.

As a Staff Scientist you set technical direction beyond your own projects. You are a builder-scientist - a creative thinker who develops novel model architectures and training paradigms and also drives their implementation and validation in real-world applications - and you raise the scientific bar for the teams around you. You will help shape the AI foundation that powers transformative products serving millions of customers.


Responsibilities

Set technical direction. Define the technical strategy and roadmap for next-generation agentic and foundation-model systems, and drive innovation and scientific excellence across the organization.

Build foundational models and agents. Design and develop foundational language models and agentic architectures that power intelligent decision-making for small and medium businesses.

Advance the science. Explore novel approaches in large-scale pretraining, fine-tuning (SFT and RLFT/GRPO), retrieval-augmented generation, and reasoning to improve factuality, adaptability, and alignment.

Solve real financial work. Build intelligent agents that understand financial data, customer context, and business workflows - from bookkeeping and autonomous accounting to planning, communication, and insight generation.

Leverage Intuits data moat. Turn Intuits unique ecosystem signals - counterparty behavior, two-sided transaction visibility, and business twins - into defensible modeling advantages.

Ship end-to-end. Take work from experimentation to production, owning evaluation frameworks and metrics that prove real-world impact.

Elevate others. Mentor scientists and engineers, review technical designs, and grow the craft of the wider team - a core expectation at the Staff level.

Collaborate across functions. Partner with AI research, engineering, product, and design to translate breakthrough science into scalable products used by millions.

Share knowledge. Publish and present research findings, internal innovations, or contributions to open-source frameworks where appropriate.