Head of AI/ML R&D — Hands-on Leader

Xplor Education; United States; In loco; USD 180.000 - 320.000; Tempo pieno

Descrizione del lavoro

Xplor—Head of AI/ML R&D role seeking a hands-on leader to own the evaluation science for every agent, the predictive and decision models behind shared AI components, and the experiments that prove value. You will balance building models with setting methods, and you’ll grow a small team of scientists while ensuring model quality and cost efficiency.

The role requires strong experience in production ML, experimentation, and multi-BU collaboration.

Competenze

• Twelve plus years in data science, ML or applied research with four+ years leading teams.
• Proven track record shipping ML/LLM/agentic systems to production.
• Experience designing robust evaluation frameworks and A/B testing.
• Strong statistics, sampling, confidence intervals and uplift modelling.
• Familiarity with AI-native toolchains and agentic coding assistants.
• Experience explaining model trade-offs to executives in business terms.

Mansioni

• Define enterprise eval methodology: golden-set design, labeling, and inter-rater agreement.
• Own quality signals: task success, groundedness, hallucination, escalation rates.
• Develop predictive models for churn, payment recovery, demand forecasting and fraud risk.
• Produce production-grade code, eval harnesses, notebooks and monitoring pipelines.
• Lead ML ethics, privacy, and security considerations across multiple BU projects.
• Mentor and grow a team of scientists/ML engineers.

Conoscenze

Data science

Machine learning

Leadership

Production ML

Experiment design

Statistics

Python/SQL

SaaS systems

Strumenti

Snowflake

Datadog

OpenTelemetry

Feature stores

Descrizione del lavoro

Xplor—Head of AI/ML R&D role seeking a hands-on leader to own the evaluation science for every agent, the predictive and decision models behind shared AI components, and the experiments that prove value. You will balance building models with setting methods, and you’ll grow a small team of scientists while ensuring model quality and cost efficiency.

The role requires strong experience in production ML, experimentation, and multi-BU collaboration.