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Credit Karma is looking for a results-oriented and strategically innovative Staff AI Scientist who is passionate about applying machine learning to address financial challenges for millions of our members. As a technical leader, you will be responsible for driving data-informed decisions and delivering AI-powered recommendation and monetization strategies that help our members achieve their financial goals. 

AI plays a ubiquitous role in Credit Karma's product, serving as the foundation for core machine learning capabilities that drive monetization, personalization, and a value-centric experience to our members. As such, this position necessitates extensive cross-functional collaboration and close partnerships across various departments, including engineering, product, marketing, finance, and analytics. 

With a strong background in data science and machine learning paired with excellent communication skills, you will drive innovation in the development and application of AI techniques that power the most relevant financial products including and actionable recommendations at Credit Karma. This role is ideal for someone who combines strong machine learning and AI skills with business acumen and cross-functional collaboration.

Responsibilities

• Partner with colleagues throughout the organization to identify high-impact opportunities to leverage our extensive data to better serve our users
• Responsible for accelerating revenue and engagement advancement through disruptive and continuous improvements in various machine learning models (targeting, marketing campaigns, etc.), feature engineering including user profiles and behavior, personalization, etc.
• Participate research efforts with other team members to explore the frontiers of GenAI, Deep Learning, Recommender systems, and other areas, as they apply to Personal Finance
• Collaborate closely with partner teams to define metrics that quantify various aspects of our business, including but not limited to revenue, engagement, user experience, etc.  Provide solid statistical bases in designing experiments.
• Represent Data Science in cross functional meetings and reviews. Be able to translate difficult technical subject matter to business partners
• Represent Credit Karma in external forums such as conferences and meetups, and act as an evangelist for CK team in such forums
• You will work with large scale Machine Learning Models to optimize for Personal Finance Products
• You will be part of a highly impactful team, who are working on large scale projects that directly impact the business and members
• You will experience both personal and professional growth as you encourage growth throughout the team

Qualifications

• MS in Computer Science, Mathematics, Statistics, Physics or a related quantitative discipline
• 7+ years of industrial experience in Data Science, Machine Learning and related areas, ideally in hyper-growth consumer Internet scenarios
• Deep statistical understanding and ability to architect and develop next-generation ML systems, staying ahead of industry trends and integrating latest advancements (e.g., GenAI)
• Strong background in programming languages (e.g., Python, Java, SQL)

Preferred Qualifications: 

• Experience with driving monetization, member engagement, longer term member value through AI
• Experience working on large scale AI systems with applications across machine learning and generative AI, AI infrastructure, data foundation, and self-serve analytics through DS methods
• Ability to balance fast paced environments at a large scale company
• Ads business and product experience

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.