Senior Data Scientist
Posted Updated
Description
About June
Hiring is still one of the slowest, most inconsistent processes inside any company. June is changing that. Our AI Interviewer runs first-round conversations with candidates at scale — structured, consistent, and fast — then hands recruiters a clear evaluation instead of a stack of resumes. We plug into the ATS teams already use (Gem, Greenhouse, Ashby, Lever, Workday, and 80+ others), and teams using June are filling roles dramatically faster than they were before.
Every interview we run makes the product smarter. That's the opportunity here.
The role
We're looking for a Senior Data Scientist to own the modeling work behind our evaluation engine. You'll be joining a small data team — small enough that what you build ships, and you'll see it affect real hiring decisions within weeks, not quarters.
This is a modeling-heavy role. You'll work on the hard, central problem at June: turning unstructured interview conversations into signals that are accurate, fair, and that hiring teams actually trust.
What you'll do
- Build, evaluate, and ship models that score and summarize candidate interviews — from problem framing through production
- Design the evaluation infrastructure that tells us whether a model is actually better: offline benchmarks, human-labeled ground truth, and online measurement
- Dig into calibration, drift, and bias. Our models influence people's careers, and we hold them to a high standard
- Partner directly with engineering and product to get models into the product, then iterate on what real usage teaches us
- Help shape how the data team works as it grows — tooling, standards, and what we choose to measure
What we're looking for
- 5+ years building and shipping ML models in production, not just in notebooks
- Strong applied experience with LLMs or NLP — fine-tuning, evaluation, prompting, retrieval, or all of the above
- Fluency in Python and SQL, and real comfort with the messy middle: ambiguous data, unclear labels, no obvious benchmark
- A rigorous instinct for evaluation. You're skeptical of your own results before anyone else is
- Clear communication with non-technical partners. You can explain why a model made a call to someone who has to defend it to a candidate
- Comfort working at startup pace, with the scope and ambiguity that comes with it
Nice to have
- Experience with fairness, bias measurement, or working under regulatory constraints (EEOC, NYC Local Law 144, EU AI Act)
- Background in HR tech, marketplaces, or another domain where model decisions carry real weight for individuals
- Experience standing up data infrastructure early at a company
Why June
You'll work on a product where the model is the product, with a short line between your work and the outcome. We move quickly, we're in the office together, and we're building something that changes how hiring actually works.