AI Data Engineer - Customer Intelligence

Join a leading fintech company that’s democratizing finance for all.

Robinhood was founded on a simple idea: that our financial markets should be accessible to all. With customers at the heart of our decisions, Robinhood is lowering barriers and providing greater access to financial information. Together, we are building products and services that help create a financial system everyone can participate in.

As we continue to build...

We’re seeking curious, growth minded thinkers to help shape our vision, structures and systems; playing a key-role as we launch into our ambitious future. If you’re invigorated by our mission, values, and drive to change the world — we’d love to have you apply.

About the team + role

The Customer Intelligence and AI (CIAI) team is a specialized Data Science unit within Corporate Strategy. Our goal is to deepen insights into customer behaviors and satisfaction using NPS, and to refine the application of data science, machine learning, and AI to improve customer experiences and operational efficiencies across the company. We partner with several teams across Robinhood including: Data, Product, Marketing, Eng, Finance, and Research. Our work drives operational efficiency, company-wide strategy and is integrated into DS/AI pipelines.

As a Customer Intelligence AI Data Engineer, you will be responsible for the development of our internal tooling infrastructure for AI use cases, supporting current ML/data pipelines and researching customer insights. This position is ideal for a data engineer or machine learning engineer with a strategy background. Candidates should be experienced in developing full stack solutions, conducting impactful research, and step up to own increasingly strategic work over time. The role offers scope for technical growth while developing strategic, AI, business and product competencies.

The role is located in the office location(s) listed on this job description which will align with our in-office working environment. Please connect with your recruiter for more information regarding our in-office philosophy and expectations.

What you’ll do

• Systems Engineering: Collaborate with our Data, Engineering, and Platform teams to establish a reusable framework designed to efficiently distribute customer insights. Your role will involve architecting and integrating both front-end and back-end systems, as well as developing data pipelines.
• Operationalizing Prompts for LLMs: Engage in the application of large language models through prompt research and design, system architecting and operationalization.
• Data Engineering/Management: Facilitate team-level reporting by establishing processes that gather and consolidate data from diverse internal and external sources, ensuring reliable and actionable information flow.
• Research: Assist in the generation of detailed team insights and in-depth analyses through targeted customer research, driving strategic initiatives and improving understanding of customer dynamics.

What you bring

• Educational Background: Possess an undergraduate or graduate degree in a quantitative field. Candidates with social science and natural science backgrounds who have shifted to heavier quant/programming roles are highly encouraged to apply.
• Professional Experience: Minimum of 3-5 years post-undergraduate or 2-3 years post-graduate experience in data engineering or full stack engineering and business strategy. Relevant positions might include technical roles at management consulting firms, investment firms, or technology companies.
• Demonstrated ability to deliver data-driven insights that have a tangible impact on business outcomes. Strong analytical capabilities are critical.
• Attention to detail with the competence to manage multiple deadlines across various projects.
• Strong ownership mentality with a consistent track record of employing creative solutions to achieve results. Must be a self-starter capable of navigating through ambiguity and adapting in a dynamic work environment.
• Excellent communication abilities, capable of driving data informed decision-making and effectively engaging with stakeholders across multiple functions.

Technical Expertise:

• Proficient in the AWS ecosystem, with substantial experience in developing, deploying, and monitoring applications within this environment.
• Advanced proficiency in Python programming, intermediate knowledge of SQL, and experience with at least one front-end framework, along with familiarity with JavaScript and HTML.
• Familiarity with the development process of machine learning models and the data science workflow.

Plus:

• AWS Certifications: Possession of an AWS certification, such as AWS Certified Developer, Solutions Architect, Data Engineer, or Dev