DataFull-time

Data Scientist

Turn usage, quality, cost, and reliability signals into product decisions that improve Direct Inference without exposing private serving details.

You will help Direct Inference understand where the product is working, where it is costly, and where customer workloads need better support.

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About Direct Inference

Direct Inference is the endpoint that does everything frontier models can do. Customers bring the SDK and model id they already use; Direct Inference handles capability, quality, cost, latency, failover, and provider churn behind the scenes.

The important product constraint is zero-knowledge: customers never see which model, provider, or version served a request. That lets them build on a stable surface while the model market keeps moving underneath it.

What you'll own

Analyze product usage, request types, cost patterns, reliability signals, and customer adoption funnels.
Build dashboards and decision-support artifacts for product, engineering, and go-to-market work.
Partner with ML and inference engineering on eval interpretation and production-quality measurement.
Identify segments where Direct Inference can reduce friction, improve quality, or lower blended cost.
Design metrics that illuminate the product without exposing private serving details.

Projects you might ship

Build a product-health dashboard that connects adoption, request mix, spend, latency, and customer outcomes.
Analyze where simple traffic can stay cheaper without hurting quality for harder workloads.
Create a privacy-aware reporting loop for request-type quality, customer cohorts, and production reliability.

What we're looking for

You have strong SQL and analytical modeling skills, with enough engineering judgment to understand data generation.
You can turn ambiguous product questions into clear analysis and recommendations.
You are comfortable with experimentation, cohort analysis, quality metrics, and cost analysis.
You write clearly and make charts or dashboards that drive decisions.
You care about privacy-aware measurement and know that not every useful signal should be exposed.

Nice to have

Experience analyzing developer products, infrastructure platforms, billing data, model quality, or marketplace dynamics.
Comfort with product instrumentation, dbt-style modeling, Python notebooks, or dashboarding systems.
A habit of turning analysis into a decision, not just a prettier chart.

Your first 90 days

Build a source-of-truth view for one major product or cost question.
Identify one actionable product or serving improvement from usage data.
Create a repeatable reporting loop for adoption, quality, reliability, or spend.

Benefits & support

Built for people doing serious work in a small team.

Competitive salary and meaningful equity

Remote-first work with San Francisco collaboration space

Medical, dental, and vision coverage

Home-office and developer-tooling budget

Quarterly team working sessions and offsites

Direct access to customers and production product decisions

Interview process

A direct loop with the people doing the work.

01

Intro

A focused conversation about your background, what you want to build, and where this role should create leverage.

02

Technical

A practical working session around the kind of problem this role owns. We prefer realistic systems over puzzle interviews.

03

Team

Meet the people you would work with across product, engineering, reliability, and customer-facing work.

04

Offer

We align on scope, compensation, start timing, and the first problems you would take on.

Application

Apply for Data Scientist.

Share the practical context we should know before the first conversation. We read applications for ownership, clarity, and evidence of shipped work.

First name *Last name *Email *PhoneLocation *Remote work locationLinkedInWebsite, GitHub, or portfolioResumeUpload PDF, DOC, or DOCXWhy this role?How did you hear about us?Are you authorized to work where you are based? *Will you now or later require visa sponsorship? *

Voluntary self-identification

These questions are optional and never affect hiring decisions. Choose “Prefer not to say” anywhere you would rather not answer.

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Candidate notices

Equal opportunity

Direct Inference is an equal-opportunity employer. Qualified applicants are considered without regard to race, color, religion, sex, gender identity or expression, sexual orientation, national origin, disability, veteran status, age, or any other status protected by law.

Reasonable accommodations

If you need an accommodation during the interview process, contact careers@directinference.com. We will work with you on a reasonable path through the process.

Candidate privacy

Application information is used for recruiting, evaluation, coordination, and hiring operations. Access is limited to people involved in the hiring process and retained only as needed for legitimate recruiting and compliance purposes.

Compensation

Posted ranges are good-faith estimates for the role and may vary by experience, location, scope, and market conditions. Equity and benefits are considered as part of the total offer.

Work authorization

Candidates are asked about current and future work authorization so we can understand employment feasibility early. A sponsorship need does not automatically disqualify a candidate.

Recruiting safety

Direct Inference will not ask candidates to purchase equipment, send money, or share sensitive personal information before a formal process. Messages should come from a directinference.com address.

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