# Head of AI and ML **Company**: Xplor **Location**: Remote, OR **Work arrangement**: remote **Experience**: executive **Job type**: full-time **Category**: Data Science **Industry**: Technology **Apply**: https://jobs.smartrecruiters.com/Xplor/744000159940989-head-of-ai-and-ml-?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply **Canonical**: https://yubhub.co/jobs/job_588227dd-f47 ## Description At Xplor, we believe that helping people make the most of each day is the most rewarding way to spend ours. We give small and medium-sized businesses cloud-based, intuitive technology solutions that enable them to manage all the hassles of running and growing a business, so business owners can get back to doing what they love. ## Position Overview The Head of AI/ML R&D decides, with evidence, whether an AI system works, how well, for whom, and at what cost. They come from data science and machine learning, and they build today with AI-native tools and agentic development. The role owns the science of evaluation across every agent, the predictive and decision models behind the shared agents (churn, demand, payment recovery, fraud), and the experiment design that lets Finance count AI value once, against a holdout. ## Key Responsibilities - Evaluation science for every agent: set the enterprise eval methodology, own the quality signals in the minimum telemetry contract, and re-run evals per BU and tenant on every agent-core upgrade. - Predictive and decision models behind the shared agents: build and run the models the horizontal pods need, choose the right tool per task, and ship models to production on the golden path. - Model economics: build the evidence behind model routing, drive cost per successful task down through distillation, caching strategy, smaller models, and prompt and context optimization. - AI-native R&D and horizon scanning: prototype varied techniques against real Xplor problems, turn what works into reference implementations in the golden path, and bring a monthly view of what has changed in models and methods. - Data science for Data-as-a-Service: shape the statistical design of benchmarks and indices, lead entity-resolution modeling with the data team. - Raise the ML fluency of the whole R&D organization: teach evals, experimentation, and probabilistic thinking, pair with BU engineers on hard model problems. ## Hands-on Expectations - Write production code, notebooks, and eval harnesses; use AI coding and agent tools daily at "platform contributor" fluency. - Build the first eval suite and the first predictive model for each golden-path exemplar. - Review model and eval pull requests from pods and BUs. - Decide on a demo, an eval, or a metric, not a deck. ## Qualifications ### Required - 12+ years in data science, machine learning, or applied research, with 4+ years leading teams of scientists or ML engineers. - Grew up in ML: has trained, evaluated, deployed, and maintained models in production. - Has shipped LLM or agentic systems to production in the last two years. - Deep experimental design and causal inference: A/B tests, holdouts, uplift modelling, matched cohorts. - Strong statistics: sampling, confidence intervals, calibration, multiple-comparison traps. - Builds with AI-native tools today and can show recent work. - Writes production-quality Python and SQL; reviews others' code credibly. - Has worked on SaaS or marketplace data at scale: multi-tenant, messy, many products. - Explains model trade-offs and uncertainty to executives in business terms. ### Preferred - Churn, retention, propensity, or pricing models in subscription or SMB software. - Payments ML: recovery, dunning optimization, fraud, or risk scoring. - Fine-tuning, distillation, or small-model work to cut inference cost. - LLM-as-judge design and validation; red-teaming of model behavior. - Snowflake (or similar) and feature stores; Datadog or OpenTelemetry for model monitoring. - Familiar with NIST AI RMF, ISO/IEC 42001, and the EU AI Act. - Published research or open-source work. ## Skills ### Required - Python - SQL - Machine Learning - Deep Learning - Experimental Design - Causal Inference - Statistics - AI-native tools - Agentic development ### Nice to have - Churn models - Retention models - Propensity models - Pricing models - Payments ML - Fine-tuning - Distillation - Small-model work - LLM-as-judge design - Snowflake - Feature stores - Datadog - OpenTelemetry - NIST AI RMF - ISO/IEC 42001 - EU AI Act --- Source: [Apply at jobs.smartrecruiters.com](https://jobs.smartrecruiters.com/Xplor/744000159940989-head-of-ai-and-ml-?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)