"Alerted.org

Overview

As part of our commitment to preventing fraud across all consumer lending & banking products, we are expanding our Consumer Risk AI Science team, which utilizes cutting-edge AI/ML technology to detect and prevent fraud while minimizing impact on good customers. As a Risk AI Scientist, you will collaborate with Risk Policy, Operations, Product, Engineering and Compliance teams to design and implement fraud mitigation risk strategies. If you're passionate about solving real customer problems through data science and modeling, we welcome you to join our talented

team.

Responsibilities

+ Design, build, evaluate, monitor, maintain, and defend machine learning models to predict and prevent various types of fraud and/or financial risk in consumer money products.

+ Collaborate with product, policy, engineering and partners across the org to understand business problems and requirements, and design machine learning modeling solutions to help the Consumer Group business grow.

+ Lead fraud/financial risk modeling for evolving consumer money products, owning the full model lifecycle and program-level outcomes.

+ Contribute to the technical strategy and decisioning roadmap across cross-functional teams supporting risk for multiple product lines.

+ Contribute to the development of scalable ML and data infrastructure to improve speed and reliability of fraud modeling efforts.

+ Help shape and implement a unified data strategy to streamline access, storage, and usability for risk and fraud use cases.

+ Research and apply innovative machine learning and statistical approaches suitable for dynamic, real-world fraud challenges.

Qualifications

+ Advanced Degree (MS and above) in Computer Science, Data Science, AI, Mathematics, Statistics, Physics or a related quantitative discipline

+ 3+ years of experience in Data Science / Machine Learning and related areas

+ Experience and deep understanding of a variety of machine learning techniques, including but not limited to, deep learning, tree-based models, reinforcement learning, clustering, time series, causal analysis, and natural language processing

+ Proficiency in deep learning ML frameworks such as TensorFlow, PyTorch, etc.

+ Ability to quickly develop a deep statistical understanding of large, complex datasets

+ Expertise in designing and building efficient and reusable data pipelines and framework for machine learning models

+ Authoritative knowledge of Python and SQL

+ Strong business problem solving, communication and collaboration skills

+ Ambitious, results oriented, hardworking, team player, innovator and creative thinker

Preferred Qualifications:

+ Relevant work experience in money fraud/credit risk, with deep understanding of money movement products, banking, finance, credit bureau, and fraud detection data

+ Experience in graph modeling

+ D. in Computer Science, Data Science, AI, Mathematics, Statistics, Physics or a related quantitative discipline

+ Experience with experimentation design and analysis

+ Working experience with public cloud platforms (AWS or GCP), Apache airflow, Vertex AI, unsupervised machine learning, anomaly detection, Scala, data-driven strategy design and optimization are all a plus

+ Experience in MLOps infrastructure and tooling

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 (https://www.intuit.com/careers/benefits/full-time-employees/) ). 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. The expected base pay range for this position is:

New York $ 172,000- 232,500

EOE AA M/F/Vet/Disability. Intuit will consider for employment qualified applicants with criminal histories in a manner consistent with requirements of local law.