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We use PPLs at Triplebyte for matching software engineers to jobs where we predict they're a strong fit. We recently published https://triplebyte.com/blog/bayes

We use PPLs at Triplebyte for matching software engineers to jobs where we predict they're a strong fit. We recently published https://triplebyte.com/blog/bayesian-inference-for-hiring-engineers https://triplebyte.com/blog/bayesian-inference-for-hiring-en... which starts to explain our framework, though PPLs would have to be a "part 2" blog post if anyone's interested.

btown 8y ago
Would love to see that follow-up. There are so many domains, like yours, where it's unlikely (from a model selection perspective) that indicators are conditionally independent; whether it's hiring candidates, or matching companies and funding sources (as we're doing at my company, Belstone - we're hiring!), or building better dating sites, or recommending products, or implementing public policies, there are underlying hidden variables that capture aptitude/appropriateness of a subject to a certain aspect of the domain. There's tons of academic literature on how to handle this, and accelerating industry support for the frameworks mentioned by OP... but the act of building an early-stage software engineering culture that is amenable to the large amounts of experimentation (often exciting, often frustrating, incredibly hard to time-predict against business needs and runway allocation) is something where I think the industry is still finding best practices. Were PPLs the right move, with the benefit of your hindsight, for that problem? Were they more promising than deep learning given challenges of properly collecting data at scale? The process of choosing a system, measuring it against more naive/heuristic approaches, deciding how to put it into production and integrate with existing software/pipelines - and reliably hiring the right people for those jobs, to make things a bit meta for Triplebyte! - that's a narrative in search of thought leaders.

btown 8y ago

Would love to see that follow-up. There are so many domains, like yours, where it's unlikely (from a model selection perspective) that indicators are conditionally independent; whether it's hiring candidates, or matching companies and funding sources (as we're doing at my company, Belstone - we're hiring!), or building better dating sites, or recommending products, or implementing public policies, there are underlying hidden variables that capture aptitude/appropriateness of a subject to a certain aspect of the domain. There's tons of academic literature on how to handle this, and accelerating industry support for the frameworks mentioned by OP... but the act of building an early-stage software engineering culture that is amenable to the large amounts of experimentation (often exciting, often frustrating, incredibly hard to time-predict against business needs and runway allocation) is something where I think the industry is still finding best practices. Were PPLs the right move, with the benefit of your hindsight, for that problem? Were they more promising than deep learning given challenges of properly collecting data at scale? The process of choosing a system, measuring it against more naive/heuristic approaches, deciding how to put it into production and integrate with existing software/pipelines - and reliably hiring the right people for those jobs, to make things a bit meta for Triplebyte! - that's a narrative in search of thought leaders.