Lead Data Scientist

About the Role

We are seeking a seasoned Leader of AI & Data Science to lead our enterprise AI portfolio spanning four specialized operations:
Generative AI & Agentic Platforms, AI Operations, Statistical & ML Modeling, and Data Center AI for our HVAC business. This leader will own the end-to-end AI strategy — from experimentation to production — ensuring AI initiatives deliver measurable business value, operate reliably at scale, and align with the company's broader digital transformation goals.

The ideal candidate combines deep technical credibility with strong people leadership, and can operate comfortably across cutting‑edge GenAI innovation, disciplined ML engineering, and domain‑specific applications in energy, HVAC, and data center environments.

• Define and execute the multi‑year AI/ML roadmap across all four operations, aligned with business priorities and P&L impact.
• Act as the senior AI voice for the organization — advising executive leadership on emerging technologies (LLMs, agentic AI, edge AI) and their business applications.
• Establish governance frameworks covering responsible AI, model risk management, data privacy, and regulatory compliance.
• Manage vendor relationships (cloud providers, LLM providers, tooling), and build‑vs‑buy decisions.

• Lead the design and scaling of an enterprise agentic AI platform (LLM orchestration, RAG pipelines, multi‑agent workflows, tool/function calling, guardrails, and evaluation frameworks).
• Drive adoption of GenAI copilots and autonomous agents across internal and customer‑facing use cases.
• Stay ahead of the rapidly evolving GenAI ecosystem (foundation models, fine‑tuning, prompt engineering, agent frameworks) and set platform standards.

• Own the operational backbone for all AI/ML workloads: CI/CD for models, model monitoring, drift detection, retraining pipelines, observability, and incident response.
• Establish SLAs/SLOs for production models and GenAI services; drive reliability, latency, and cost optimization (including LLM inference cost management).
• Standardize the ML platform stack (feature stores, model registries, experiment tracking, deployment patterns) across the organization.

• Oversee development of statistical, forecasting, and machine learning models supporting core business functions (e.g., demand forecasting, pricing, churn, service optimization, predictive maintenance).
• Ensure rigor in model development — experimental design, validation, explainability, and measurable business KPIs.
• Partner with business unit leaders to prioritize a portfolio of high‑ROI analytics use cases.

• Lead AI/ML initiatives focused on data center development and operations — thermal management, cooling optimization, energy efficiency, capacity planning, and predictive insights for HVAC systems.
• Work closely with HVAC product, engineering, and field teams to translate sensor/telemetry data (BMS, IoT, chillers, CRAH/CRAC units) into actionable intelligence and product features.
• Develop digital twin, anomaly detection, and optimization models that improve PUE, uptime, and equipment lifecycle for data center customers.

• Hire, mentor, and grow POD leads and a multidisciplinary team of data scientists, ML engineers, GenAI engineers, and MLOps engineers.
• Foster a culture of experimentation, engineering excellence, and business accountability.
• Define career paths, performance standards, and knowledge‑sharing practices across PODs.

• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Statistics, or a related field (PhD a plus).
• 12+ years of experience in data science / machine learning, with 5+ years in senior leadership managing multiple teams or PODs.
• Proven track record of delivering production‑grade AI/ML systems at enterprise scale.
• Hands‑on familiarity with the modern GenAI stack: LLMs (OpenAI, Anthropic, open‑source), RAG, vector databases, agent frameworks (e.g., Lang Graph, CrewAI, Auto Gen), and evaluation/guardrail tooling.
• Strong grounding in MLOps/AIOps…