Senior GenAI Data Scientist

As a Senior GenAI Data Scientist at Intuit Credit Karma, your main responsibilities will include conducting cutting-edge research on generative AI and Natural Language Processing. You'll be tasked with developing and fine-tuning large language models (LLMs), optimizing architectures for performance, and collaborating across teams to communicate complex ideas. Additionally, you'll be instrumental in defining the long-term research vision, and keeping your skills sharp by engaging in ongoing learning and contributing to the research community.

Candidates applying for the Senior GenAI Data Scientist role at Intuit Credit Karma should possess a Master's degree or Ph.D. in AI, Data Science, Computer Science, Statistics, Mathematics, or similar fields. Additionally, substantial experience in GenAI, LLM research, and hands-on programming skills in languages like Python, R, or Java are essential. A proven track record in leading research projects and familiarity with AI libraries such as PyTorch or TensorFlow will further enhance your application.

At Intuit Credit Karma, the process for research and development begins with staying up-to-date on the latest advancements in GenAI technologies. As a Senior GenAI Data Scientist, you'll identify key research areas aligned with the company's goals, design innovative architectures, and implement new algorithms. You'll actively collaborate with cross-functional teams, translating your findings into actionable insights that influence product development and enhance user experiences.

Joining Intuit Credit Karma as a Senior GenAI Data Scientist comes with an array of excellent benefits. Employees enjoy comprehensive medical and dental coverage, retirement plans, commuter benefits, wellness perks, and generous Paid Time Off options. The company also offers education perks and a paid gift week in December, all designed to promote a balanced and fulfilling work-life experience.

At Intuit Credit Karma, a Senior GenAI Data Scientist will find numerous opportunities for professional growth. The company fosters a culture of continuous learning and encourages participation in conferences and community engagement. You’ll have opportunities to mentor team members, lead projects, and contribute to publications, which will not only enhance your expertise but also elevate the entire team's capabilities in the rapidly evolving GenAI landscape.

In your response, focus on a specific project where you played an integral role. Highlight the problem you solved, the methodologies you used, and the outcomes achieved. Discuss key takeaways from this experience regarding the development and optimization of models.

Discuss the various techniques you apply for fine-tuning, such as adjusting hyperparameters, data augmentation, and implementing transfer learning. Emphasize your understanding of ensuring quality datasets while mitigating biases during the fine-tuning process.

Explain your strategies for keeping up with industry trends. You could mention attending conferences, participating in webinars, reading research papers, or being part of professional communities focused on AI and machine learning.

Provide an overview of your proficiency in Python and specific libraries like TensorFlow or PyTorch. Illustrate how you have utilized these tools in past projects, detailing any challenges faced and how you overcame them.

Stress the importance of teamwork in your projects. Speak on how you effectively communicate complex research concepts to diverse teams and how collaboration has sparked innovation in your previous experiences.

Take this opportunity to discuss your commitment to ethics in AI. Describe measures you take to evaluate bias, ensure transparency in model development, and the importance of compliance with ethical standards in all projects.

Select a challenging project you led or contributed to, touching on the specifics of the issue. Describe the steps you took to address the problem and the lessons learned that could benefit future projects.

Highlight your past experiences in leading research projects. Discuss your approach to defining goals, motivating team members, and how you effectively communicated results to stakeholders.

Talk about the various metrics you utilize to gauge model performance, including accuracy, F1 score, or user engagement levels. Discuss how these metrics guide your iterative process of model enhancement.

Express your passion for financial technology and the opportunity to impact members' lives positively. Discuss how Credit Karma's mission aligns with your professional values and aspirations in generative AI.