Asks for

The ad says“You own your own pipeline work end-to-end. You're comfortable with schema design, dbt models, and pipeline triage. You don't wait for a data engineer to unblock you on basics. If something upstream is broken, you can diagnose it, fix it, or spec the fix precisely.”
The ad says“Own our experimentation framework and standards across squads. Define how we design, run, and evaluate experiments, setting the bar for what a well-run test looks like, driving consistency across the org, and directly owning the highest-stakes analyses.”
The ad says“You've shipped ML in production. Not just trained models — you've integrated them into product workflows, seen what breaks at scale, and know what good ML infrastructure looks like. You can direct and evaluate ML engineering work, even when you're not the primary builder. Content understanding — taking unstructured voice, text, and behavior data and turning it into something the product can act on — is the kind of problem you want to work on.”
The ad says“You're technically deep. You have 7+ years of experience as a data scientist. You're highly fluent in SQL and Python — not just for analysis, but for influencing engineering decisions, defining data infrastructure standards, and being a credible technical voice in system design conversations. You're comfortable operating across an end-to-end data pipeline: you can triage pipeline issues, spec data engineering work, and lead a data engineer through complex problems when needed. You've designed and analyzed experiments, know the difference between a well-run test and a noisy one, and have deep expertise in predictive modeling and ML techniques.”
The ad says“You're technically deep. You have 7+ years of experience as a data scientist. You're highly fluent in SQL and Python — not just for analysis, but for influencing engineering decisions, defining data infrastructure standards, and being a credible technical voice in system design conversations. You're comfortable operating across an end-to-end data pipeline: you can triage pipeline issues, spec data engineering work, and lead a data engineer through complex problems when needed. You've designed and analyzed experiments, know the difference between a well-run test and a noisy one, and have deep expertise in predictive modeling and ML techniques.”
The ad says“You're technically deep. You have 7+ years of experience as a data scientist. You're highly fluent in SQL and Python — not just for analysis, but for influencing engineering decisions, defining data infrastructure standards, and being a credible technical voice in system design conversations. You're comfortable operating across an end-to-end data pipeline: you can triage pipeline issues, spec data engineering work, and lead a data engineer through complex problems when needed. You've designed and analyzed experiments, know the difference between a well-run test and a noisy one, and have deep expertise in predictive modeling and ML techniques.”
The ad says“You own your own pipeline work end-to-end. You're comfortable with schema design, dbt models, and pipeline triage. You don't wait for a data engineer to unblock you on basics. If something upstream is broken, you can diagnose it, fix it, or spec the fix precisely.”
The ad says“You're technically deep. You have 7+ years of experience as a data scientist. You're highly fluent in SQL and Python — not just for analysis, but for influencing engineering decisions, defining data infrastructure standards, and being a credible technical voice in system design conversations. You're comfortable operating across an end-to-end data pipeline: you can triage pipeline issues, spec data engineering work, and lead a data engineer through complex problems when needed. You've designed and analyzed experiments, know the difference between a well-run test and a noisy one, and have deep expertise in predictive modeling and ML techniques.”

About us

Alpha is a product studio focused on the intersection of AI and consumer social — backed by a16z and many of the top investors in the world. Our goal is to create social products that we use, that our friends use, and that have the potential to reach billions of people.

Clubhouse was our first app, and we’ve been quietly growing that while building our next set of products. We have years of runway, millions of users, and find ourselves at a very interesting time, where AI is making entirely new things possible with voice.

If you are a talented builder who’s interested in joining us, we’d love to talk.

The role

You'll be the technical anchor for our data science function. Beyond your squad work, you'll hold the technical bar for the whole team — metric integrity, pipeline quality, analytical rigor, team practices. You'll have real license to shape how we evolve.
You'll be embedded in our Discovery squad — owning the ML and content understanding work that shapes how people find the right rooms, the right hosts, and the right moments on Clubhouse.
You'll help shape where the team goes next. As content-understanding Discovery initiatives mature, you'll help determine what additional DS scope needs to exist — including how data licensing work evolves, what contractor support looks like over time, and where new ML and platform investments should land.
You’ll provide mentorship and thought partnership to our senior data scientists, and operate with the autonomy that comes with setting direction, not just executing within it.
We are a remote-friendly company — with a strong commitment to ensuring people can do great work here and thrive without having to live near an office. This role is open to candidates across the U.S.

Who you are

You're technically deep. You have 7+ years of experience as a data scientist. You're highly fluent in SQL and Python — not just for analysis, but for influencing engineering decisions, defining data infrastructure standards, and being a credible technical voice in system design conversations. You're comfortable operating across an end-to-end data pipeline: you can triage pipeline issues, spec data engineering work, and lead a data engineer through complex problems when needed. You've designed and analyzed experiments, know the difference between a well-run test and a noisy one, and have deep expertise in predictive modeling and ML techniques.
You've shipped ML in production. Not just trained models — you've integrated them into product workflows, seen what breaks at scale, and know what good ML infrastructure looks like. You can direct and evaluate ML engineering work, even when you're not the primary builder. Content understanding — taking unstructured voice, text, and behavior data and turning it into something the product can act on — is the kind of problem you want to work on.
You care about data trust the way engineers care about code quality. You have instincts and opinions about metric integrity, pipeline health, dashboard sprawl, and the team practices that keep data honest as a team scales. You don't tolerate a messy data environment, and you have strong priors on how to clean one up.
You own your own pipeline work end-to-end. You're comfortable with schema design, dbt models, and pipeline triage. You don't wait for a data engineer to unblock you on basics. If something upstream is broken, you can diagnose it, fix it, or spec the fix precisely.
You think like a product partner, not a service org. You don't wait for requests — you proactively identify what questions matter, size opportunities, and help product managers figure out where to place bets. You have a healthy skepticism about data, know when something doesn't add up, and dig in until you find the right answer.
You communicate with clarity. You can take a messy analysis and turn it into a clear recommendation. You write well. You build dashboards and visualizations that people actually use to make decisions.
You thrive with autonomy. You will set the agenda for what the team works on and help shape which bets the company makes. You thrive on turning ambiguity into a roadmap.
You ship. You have a bias toward getting things done. You'd rather have a good-enough answer today than a perfect one in two weeks.
You are a team player. You are low-ego, put the team’s success first, and believe that fostering a collaborative and diverse environment is not only integral to the team's well-being but also crucial for driving innovation and achieving long-term success.

What you will do

Own Discovery's data work. You'll be the DS lead embedded in our Discovery squad, owning ML and content understanding, XP readouts, metric ownership, and strategic opportunity sizing for the product area.
Lead our predictive modeling and ML work. You'll own the models that shape product direction and, over time, help shape the infrastructure that supports them.
Set and hold the bar for metric integrity across the team. Metric definitions, pipeline trust, dashboard quality, team practices — you'll own the standards that keep our data honest as we scale.
Shape the team's future. Advocate for the structural changes you think the function needs. You won't own headcount decisions, but you'll be the technical voice that shapes them.
Own our experimentation framework and standards across squads. Define how we design, run, and evaluate experiments, setting the bar for what a well-run test looks like, driving consistency across the org, and directly owning the highest-stakes analyses.
Drive strategic opportunity sizing across the company. You’ll proactively identify where we should place bets and make data a proactive product partner. You’ll synthesize signals across product areas and help leadership prioritize before roadmaps are locked.
Own company-wide, top-line metric strategy and drive the data changes needed to ensure high data coverage and accurate metric forecasting for our quarterly and yearly goals.
You’ll grow, teach, and improve rapidly. At Clubhouse you’ll be working with an incredible team, who love to learn from and teach each other. You’ll find yourself stretching to new areas, immersed in feedback, and growing your skills on a daily basis.
You’ll create something meaningful. Social products allow people to connect in ways that were never before possible. When the telephone was invented, it suddenly allowed people to speak with others across the world. Thanks to FaceTime, grandparents everywhere can now watch their grandchildren grow up. At Clubhouse, you’ll be figuring out something entirely new for the world — a positive product that brings people’s voices together to help everyone have a life filled with great friendships.

Our company is proud to be an equal opportunity employer. We do not discriminate in hiring or make any employment decision based on race, color, religion, national origin, age, sex (including pregnancy, childbirth, or related medical conditions), marital status, ancestry, physical or mental disability, genetic information, veteran status, gender identity or expression, sexual orientation, or other applicable legally protected characteristics. We are committed to providing reasonable accommodations for qualified individuals with disabilities in our job application procedures. If you need assistance or an accommodation due to a disability, you may contact us at recruiting@clubhouse.com.