The company aims to help businesses understand and optimize their customer journey for the AI era, ensuring they perform well through the lens of customer AI tools. They need to build analyses, models, prototypes, and AI systems to provide accurate, timely, and powerful analyses of risks and opportunities, suggesting specific actions for improvement.
Requirements
- Experiencing building meaningful ML/AI-powered systems, like recommenders, classifiers, sophisticated information retrieval or ranking systems, etc. from zero to one (at a startup or inside an existing enterprise.)
- Experience leading teams — whether large or small – of researchers, data scientists, or AI/ML engineers – in a fast-paced environment.
- Excellent analytical skills and familiarity with modern analysis tools, using SQL on CDWs (e.g. BigQuery, Snowflake, Clickhouse), cloud-based notebook computing, pandas or polars, and more.
- Experience building AI systems using LLMs, VLMs, or smaller transformer-based models – in a rigorous way, starting with looking at the data! But ideally moving on to building eval suites and setting up ongoing performance monitoring.
- A scientific, but pragmatic approach to experimentation and analysis
- Experience with, and a nuanced appreciation for the strengths and weaknesses of, modern consumer/prosumer AI chatbots, search tools and agents a plus
Responsibilities
- directly build analyses, models, prototypes and AI systems
- build analyses and recommendation systems
- create a culture and technical environment where we can continuously experiment with data & AI-powered capabilities
- Be hands on writing code – in notebooks or even in production – to test new models, prompts, and systems
- Mentor AI & data engineers to be more effective at working with AI/ML systems
- create analyses, reports, posts & videos that help advance the ecosystem's understanding of AI and how to evolve to meet the rapidly changing business environment AI is driving
- Run analyses and experiments to inform your own work and the work of the entire company
Other
- build teams (and as importantly: cultures)
- build a culture and technical environment where we can continuously experiment with data & AI-powered capabilities
- Collaborate with engineers, PMs, and with cross-functional teams and even customers to understand where adding intelligence to the product can help them
- Mentor AI & data engineers to be more effective at working with AI/ML systems – better analysts, more rigorous experimenters, and with more tools in their toolbelt.
- Collaborate with our CTO, CEO, and marketing teams to create analyses, reports, posts & videos that help advance the ecosystem's understanding of AI and how to evolve to meet the rapidly changing business environment AI is driving
