Great question. The CPG data landscape is a fragmented mess. We're actually tackling this at arena (https://arena-ai.com) to build out AI foundation models for the industry. We have some tips and potentially tools that might be useful to you. Would be great to get your input / hear your pain points if you're up for it
The subluminal application is particularly interesting, because it seems almost feasible. Is there a practical path to meeting these mass/energy requirements though?
Arena | Lead Machine Learning Scientist + Deep Learning Scientist | Full-time | Remote / NYC
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We founded Arena a little over a year ago, to bring advanced ML techniques out of the lab (lots of active learning, for instance), and apply them at scale, for large enterprises where we can measurably prove impact. We're profitable, with multiple large scale Fortune 500 customers in pilots & in production.
There are 8 of us on the team, almost entirely technical – a mix of Machine Learning Engineers and Scientists with deep academic background and experience. The team includes 3 former founders. Co-founder and CEO founded Kimono Labs (YC W14, acquired by Palantir).
Competitive compensation, with material equity ownership for the right fit.
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Interesting, thanks for putting this together. Curious – have you found good sources of granular data for credit card spending and movement patterns, perhaps from traffic/ride-sharing/mobile (that's anonymous/protects user privacy ofc)?
