I've been following the gut microbiome space for close to 6 years now, and still am not convinced that there's anything humans can materially do to improve their gut microbiome, aside from eating more vegetables and less meat. Even the word "improve" is a misnomer - we don't quite know what's good bacteria and what's bad bacteria for a specific individual.
Additionally, as with all gut microbiome experiments - what happens in the lab in mice on mice might not translate to humans - and the human gut microbiome is extremely messy and tough to measure. We just don't have the longitudinal data across human populations to make quantifiable decisions about our gut microbiome.
I always like to draw the analogy to cigarettes and lung cancer. There's a very clear causal link to smoking and lung cancer after 20 years later. We don't have any such causal link between gut microbiome alterations and health outcomes.
> derided researchers in machine learning who use purely statistical methods to produce behavior that mimics something in the world, but who don't try to understand the meaning of that behavior.
It's crazy how wrong Chomsky was about machine learning. Maybe the real truth is that humans are stochastic parrots who have an underlying probability distribution - and because gradient descent is so good at reproducing probability distributions - LLMs are incredibly good at reproducing language.
I have a lot of experience (5 years) building and optimizing data pipelines from diverse sources using Python, SQL, Snowflake, and AWS Aurora that supported compliance and performance monitoring. I'm an SF native, and can start immediately.
I have a lot of experience (5 years) building and optimizing data pipelines from diverse sources using Python, SQL, Snowflake, and AWS Aurora that supported compliance and performance monitoring. I live in San Francisco and can start immediately.
I have a lot of experience (5 years) building and optimizing data pipelines from diverse sources using Python, SQL, Snowflake, and AWS Aurora that supported compliance and performance monitoring. I'm an SF native, and can start immediately.
I have a lot of experience (5 years) building and optimizing data pipelines from diverse sources using Python, SQL, Snowflake, and AWS Aurora that supported compliance and performance monitoring.
I've implemented schema-drift detection, null tracking, reconciliation layers, and metric QA. I've built automated QA layers in Python and SQL, and have ample experience aligning with Product, QA, compliance, and analytics teams on metric definitions and data products.
I saw the position for the GTM Data Analyst, and think I'm a good fit. I have 4+ years of relevant experience (Python, SQL, Tableau, Snowflake) building data pipelines and dashboards for healthcare and semiconductor companies. I also live in San Francisco about half a mile away from CloudTrucks HQ, and don't require any sponsorship.
Role: Data Analyst / Product Data Scientist / Analytics Engineer
Experience: 4+ years
Data Analyst with 4+ years of experience translating data into product and business impact. I've built end-to-end analytics workflows at Nevro and Qualcomm — from SQL/Python pipelines to stakeholder-facing dashboards and KPI frameworks. I specialize in experimentation, A/B testing, data visualization, and making messy data useful for product and growth teams. Looking for in-person (in the Bay Area) or remote-friendly roles in product analytics, data science, or analytics engineering where I can work cross-functionally and ship insights that move the needle.
Role: Data Analyst / Product Data Scientist / Analytics Engineer
Experience: 4+ years
Data Analyst with 4+ years of experience translating data into product and business impact. I've built end-to-end analytics workflows at Nevro and Qualcomm — from SQL/Python pipelines to stakeholder-facing dashboards and KPI frameworks. I specialize in experimentation, A/B testing, data visualization, and making messy data useful for product and growth teams. Looking for in-person (in the Bay Area) or remote-friendly roles in product analytics, data science, or analytics engineering where I can work cross-functionally and ship insights that move the needle.