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Bangalore, Karnātaka, India
Actively hiring, first seen by us 1 day agoAs a Senior AI Scientist on BI, you will own AI/ML solutions end to end, from data curation through evaluation to production, applied directly to BI's reporting and financial-analysis surfaces.
Own end-to-end AI/ML solutions for BI surfaces, from modeling to production
Curate task-specific datasets from proprietary financial data with governance built in
Build evaluation benchmarks for BI use cases such as financial commentary accuracy
Design AI-native features balancing latency, cost, and quality, including Omni conversational BI
Apply causal inference, time-series, and NLP/LLM methods to anomaly detection and consolidation insights
As a Senior AI Scientist on BI, you will own AI/ML solutions end to end, from data curation through evaluation to production, applied directly to BI's reporting and financial-analysis surfaces. You will work closely with BI's product, design, and engineering teams to bring machine learning and LLM-based approaches to problems like generating financial narratives, surfacing benchmarking insights on KPI Scorecards, and improving forecasting for Cash Flow and Budgets.
Own delivery of end-to-end AI/ML solutions for BI surfaces, spanning modeling, evaluation, and production integration.
Curate task-specific datasets from Intuit's proprietary financial data, with data governance and privacy built in
build evaluation benchmarks specific to BI use cases, such as the accuracy of generated financial commentary or KPI explanations
Design AI-native features that balance latency, cost, and quality, including features embedded in agentic pipelines such as Omni conversational BI
Track emerging research and apply novel ML methods, including causal inference, time-series forecasting, and NLP/LLMs, to BI problems such as anomaly detection in financial statements and multi-entity consolidation insights.
4+ years of applied ML or data science experience, ideally including work with financial, accounting, or structured tabular and time-series data.
Hands-on experience with LLMs and agentic AI systems, including prompting, fine-tuning, and evaluation of agent pipelines, alongside classical ML methods such as supervised and unsupervised learning, time-series forecasting, and anomaly detection.
Experience building end-to-end, reusable ML pipelines, from data acquisition and ETL through model deployment and monitoring.
Proficient in Python and SQL, comfortable in a Linux environment, and experienced with large-scale distributed data systems such as Spark or Hive; familiarity with OLAP engines such as StarRocks is a plus.
Qualifications - MS or PhD in Computer Science, Statistics, Applied Mathematics, Operations Research, or a related field, or equivalent practical experience. - 4+ years of applied ML or data science experience, ideally including work with financial, accounting, or structured tabular and time-series data.
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- Hands-on experience with LLMs and agentic AI systems, including prompting, fine-tuning, and evaluation of agent pipelines, alongside classical ML methods such as supervised and unsupervised learning, time-series forecasting, and anomaly detection. - Proficient in Python and SQL, comfortable in a Linux environment, and experienced with large-scale distributed data systems such as Spark or Hive; familiarity with OLAP engines such as StarRocks is a plus. - Experience building end-to-end, reusable ML pipelines, from data acquisition and ETL through model deployment and monitoring.
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We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Intuit's Business Intelligence (BI) team builds the reporting and analytics experiences that millions of QuickBooks customers and accountants rely on every day, including Modern Reports, Management Reports, KPI Scorecards, Dashboards, and FP&A (Cash Flow, Budgets, Forecasting). The team is in the middle of extending these experiences with AI: conversational reporting through Omni, self-serve report building, and agentic development practices that let us build faster on a codebase spanning years of financial logic. We sit on one of Intuit's largest proprietary datasets, tens of billions of transaction rows across millions of small businesses, and we are scaling our platform (built on StarRocks) to support multi-entity, multi-currency customers at a new order of magnitude.
As a Senior AI Scientist on BI, you will own AI/ML solutions end to end, from data curation through evaluation to production, applied directly to BI's reporting and financial-analysis surfaces. You will work closely with BI's product, design, and engineering teams to bring machine learning and LLM-based approaches to problems like generating financial narratives, surfacing benchmarking insights on KPI Scorecards, and improving forecasting for Cash Flow and Budgets.
Responsibilities
Own delivery of end-to-end AI/ML solutions for BI surfaces, spanning modeling, evaluation, and production integration.
Select appropriate algorithms and approaches for financial and reporting problems, explain trade-offs, and identify common failure modes such as hallucination, bias, and drift.
Curate task-specific datasets from Intuit's proprietary financial data, with data governance and privacy built in, and build evaluation benchmarks specific to BI use cases, such as the accuracy of generated financial commentary or KPI explanations.
Design AI-native features that balance latency, cost, and quality, including features embedded in agentic pipelines such as Omni conversational BI, and design evaluation approaches for non-deterministic agentic flows.
Track emerging research and apply novel ML methods, including causal inference, time-series forecasting, and NLP/LLMs, to BI problems such as anomaly detection in financial statements and multi-entity consolidation insights.
Use AI agents as part of your own science workflow, including experiment orchestration, hyperparameter search, and simulation-based validation, and help set standards for which BI science tasks should be agent-driven.
Partner with BI product, design, and engineering to shape what's worth building, grounded in usage and feedback signals from reporting surfaces.
Apply responsible AI practices given that BI surfaces present financial figures directly to accountants and business owners, and mentor others on the same.
Qualifications
MS or PhD in Computer Science, Statistics, Applied Mathematics, Operations Research, or a related field, or equivalent practical experience.
4+ years of applied ML or data science experience, ideally including work with financial, accounting, or structured tabular and time-series data.
Hands-on experience with LLMs and agentic AI systems, including prompting, fine-tuning, and evaluation of agent pipelines, alongside classical ML methods such as supervised and unsupervised learning, time-series forecasting, and anomaly detection.
Proficient in Python and SQL, comfortable in a Linux environment, and experienced with large-scale distributed data systems such as Spark or Hive; familiarity with OLAP engines such as StarRocks is a plus.
Experience building end-to-end, reusable ML pipelines, from data acquisition and ETL through model deployment and monitoring.
Strong written and verbal communication skills, with the ability to explain technical and financial concepts to both technical and non-technical audiences.
Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may 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.
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Bangalore, Karnātaka, India
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