Lead Data Scientist

Johnson Controls, Inc.; Neuhausen am Rheinfall; Vor Ort; CHF 280.000 - 420.000; Vollzeit

Zusammenfassung

Johnson Controls, Inc. seeks a seasoned Leader of AI & Data Science to direct an enterprise AI portfolio across four operations: GenAI & Agentic Platforms, AI Operations, Statistical & ML Modeling, and Data Center AI for HVAC.

The role demands deep technical credibility, strong people leadership, and the ability to align cutting-edge AI initiatives with business value, governance, and scalable production systems in energy and data center environments.

Qualifikationen

• Bachelor's or Master's in CS/DS/Engineering; PhD a plus.
• 12+ years in data science / ML; 5+ years in senior leadership.
• Proven production AI/ML at enterprise scale.
• Hands-on GenAI stack experience.
• Strong collaboration with business stakeholders and executives.

Aufgaben

• Define and execute multi-year AI/ML roadmap aligned with business priorities.
• Advise executive leadership on emerging technologies and business applications.
• Establish governance for responsible AI and model risk.
• Manage vendor relationships and build-vs-buy decisions.
• Lead design and scaling of GenAI platform and agentic workflows.
• Oversee ML operations: CI/CD, monitoring, drift detection, retraining.
• Develop AI/ML models for demand forecasting, pricing, and maintenance.
• Lead data center AI initiatives for thermal management and energy efficiency.
• Mentor POD leads and foster innovation and accountability.

Kenntnisse

Strategic leadership

Executive communication

AI strategy & governance

MLOps & cloud platforms

Vendor management

Ausbildung

Bachelor's or Master's in CS/DS/Engineering

Tools

OpenAI/Anthropic/OSS LLMs

RAG pipelines

Vector databases

LangGraph/CrewAI/AutoGen

AWS/Azure/GCP

Jobbeschreibung

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.

Key Responsibilities

Strategic Leadership

• 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.

GenAI & Agentic Platform

• 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.

AI Operations

• 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.

Statistical & ML Models for Business

• 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.

Data Center AI for HVAC Business

• 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.

People & Organizational Leadership

• 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.

Required Qualifications

• 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., LangGraph, CrewAI, AutoGen), and evaluation/guardrail tooling.
• Strong grounding in MLOps/AIOps practices and cloud platforms (AWS / Azure / GCP), containerization, and CI/CD for ML.
• Solid foundation in classical statistics and ML (regression, time series, classification, optimization) and their application to business problems.
• Demonstrated ability to partner with business stakeholders, translate ambiguous problems into AI solutions, and communicate impact to executives.
• Experience managing budgets, vendors, and cross‑functional programs.

Preferred Qualifications

• Domain experience in HVAC, data centers, energy management, building automation, or industrial IoT.
• Familiarity with time‑series/telemetry data at scale, digital twins, physics‑informed ML, or reinforcement learning for control/optimization (e.g., cooling optimization).
• Experience with edge AI deployment and BMS/SCADA integration.
• Knowledge of responsible AI frameworks, model governance, and relevant regulations.
• Publications, patents, or recognized contributions in AI/ML.

Key Competencies

• Strategic thinking with hands‑on technical depth
• Multi‑team leadership and talent development
• Executive communication and stakeholder management
• Bias for action and outcome‑driven delivery
• Comfort with ambiguity in a fast‑evolving AI landscape

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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.

Key Responsibilities

Strategic Leadership

• 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.

GenAI & Agentic Platform

• 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.

AI Operations

• 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.

Statistical & ML Models for Business

• 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.

Data Center AI for HVAC Business

• 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.

People & Organizational Leadership

• 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.

Required Qualifications

• 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., LangGraph, CrewAI, AutoGen), and evaluation/guardrail tooling.
• Strong grounding in MLOps/AIOps practices and cloud platforms (AWS / Azure / GCP), containerization, and CI/CD for ML.
• Solid foundation in classical statistics and ML (regression, time series, classification, optimization) and their application to business problems.
• Demonstrated ability to partner with business stakeholders, translate ambiguous problems into AI solutions, and communicate impact to executives.
• Experience managing budgets, vendors, and cross‑functional programs.

Preferred Qualifications

• Domain experience in HVAC, data centers, energy management, building automation, or industrial IoT.
• Familiarity with time‑series/telemetry data at scale, digital twins, physics‑informed ML, or reinforcement learning for control/optimization (e.g., cooling optimization).
• Experience with edge AI deployment and BMS/SCADA integration.
• Knowledge of responsible AI frameworks, model governance, and relevant regulations.
• Publications, patents, or recognized contributions in AI/ML.

Key Competencies

• Strategic thinking with hands‑on technical depth
• Multi‑team leadership and talent development
• Executive communication and stakeholder management
• Bias for action and outcome‑driven delivery
• Comfort with ambiguity in a fast‑evolving AI landscape