This profile has not been claimed and data may not be accurate.
5th percentile
ML/AI data scientist building consumer-scale systems at FanDuel
Credibility
GTM / Distribution · 12th percentile
- fanduel.com ranks #7,408 on the Majestic Million (global domain prominence).
Relationship Matrix
Founder score
- 95%Probability of being accurate (95%)fanduel.com ranks #7,408 on the Majestic Million (global domain prominence).
What you likely need
You're a Data Scientist and ML/AI practitioner at FanDuel, working at the intersection of machine learning and consumer entertainment. Events focused on applied ML, data infrastructure, and AI product development would be most relevant to your current work. Connecting with other technical practitioners building ML systems at scale — especially in consumer tech and gaming — would be high-value.
Would you attend these IRL Festival events?
We will tune and customize your Founder Festival experience to make sure you get invited to the events that would be most valuable to you.
- tacticalA dinner for ML/AI practitioners at consumer tech companies to share applied machine learning techniques and tooling in production environments.
- tacticalA roundtable for data scientists in sports tech and gaming to discuss real-time ML systems, personalization, and predictive modeling at scale.
- introsAn NYC-based happy hour for AI/ML engineers and data scientists building consumer-facing products to swap notes on model deployment and experimentation.
- introsA Vanderbilt alumni networking dinner in NYC focused on tech and data science careers to build a local professional network.
- introsA dinner with founders and operators building in the sports, gaming, and entertainment tech space to explore the startup ecosystem adjacent to your current role.
- positioningA workshop for senior ML practitioners exploring paths from IC data science roles into founding, product leadership, or applied research.
- tacticalAn NYC meetup for power users of modern AI development tools — LLM APIs, vector databases, and ML infrastructure — to share workflows and best practices.
