ALBUM

About Halda

Halda is the AI student engagement platform that drives yield. We help universities turn prospects into enrolled, well-matched students through a system that compounds: every interaction is aware of the last, so the platform gets smarter about each student over time.

Modeling sits at the center of that promise. Our predictions tell partners which students to engage, when, and how. We're a fast-moving company building category-defining technology in higher education, and we're hiring a VP of Data Science to own it.

The Role

You'll own modeling for our predictive products and services end to end, from research to production to the partner-facing outcomes they create. You'll set the data and analytics product roadmap, and you'll be a trusted technical partner to the account managers and university clients who put our models to work.

This is a builder's seat. You'll shape the strategy, hire the team, and ship the models that move enrollment numbers for real institutions.

Ownership and Leadership Opportunity

We sell outcomes. Each package — Inquiry, Application, Yield — is an ARR line a team owns, with one platform underneath. You'll own two of the most important pieces, and your scope grows with the revenue they generate.

• Yield product. Own the Yield outcome package end to end. Set the strategy, build the roadmap, and ship the features behind the admit-to-enroll model partners pay for. You're accountable to the ARR it brings in.
• Platform. Own the platform that sits under every package (integrations, AI engine, reporting). Set its direction and build the roadmap that makes every outcome line stronger.
• Revenue accountability. Carry direct revenue accountability for the data products you deploy, with the metrics and reporting to prove the impact.
• Room to grow. Grow your scope, your team, and your mandate as the data products scale, with a clear path to own more outcome lines as ARR climbs.

What You'll Own

Predictive modeling

• Model lifecycle. Own the full lifecycle of our predictive models, from problem framing and feature design through deployment, monitoring, and retraining.
• Core models. Build and improve the propensity, yield, and student-fit models that power engagement decisions across the platform.
• Rigor. Set the standard for model quality, validation, and measurement so partners trust the numbers.

Product and roadmap

• Roadmap. Own the roadmap for our analytics and data products. Decide what we build, in what order, and why.
• Productize. Translate modeling capability into product features and services that partners can act on.
• Platform. Partner with engineering and product to ship data infrastructure that scales with our partner base.

Partners and account teams

• Partner-facing. Work directly with our partner success team and university partners as we deliver modeling for their enrollment goals.
• Communication. Translate model outputs into clear, confident guidance that non-technical stakeholders can act on.
• Feedback loop. Use partner feedback and results to sharpen the next generation of models.

Team and leadership

• Build the team. Hire, mentor, and grow a high-caliber data science team.
• Set direction. Set the technical vision and the bar for execution.

What You Bring

Required

• 5+ years of data analytics/science experience
• Higher ed. Higher education experience. You understand enrollment, the student journey, and how institutions actually operate.
• Production ML. A track record of shipping predictive models into production, not just research or notebooks.
• Leadership. Senior data science leadership experience, with a history of building and leading teams.
• Communication. Fluency translating modeling work into business outcomes and explaining it to non-technical audiences.
• Technical depth. Strong foundations in statistics, machine learning, and experimentation (A/B testing, causal inference).

Ideal

• Startup speed. Experience at a fast-paced startup, comfortable with ambiguity and building from zero to one.
• MLOps. Hands-on experience with modern ML/MLOps tooling and the model lifecycle at scale (monitoring, drift, retraining).
• Data governance. Working knowledge of FERPA and student-data privacy, plus the governance that protects it.
• AI fluency. Familiarity with LLMs and applied GenAI, and a point of view on where they help and where they don't.
• Product sense. Product instincts, with experience owning or co-owning a data product roadmap.

Benefits

• Remote or Hybrid Role
• Comprehensive medical, dental, and vision coverage.
• Unlimited PTO