Homeoffice Expert Machine Learning Engineer, Data and Analytics bei Keysight Technologies, Inc.

Keysight is on the forefront of technology innovation, delivering breakthroughs and trusted insights in electronic design, simulation, prototyping, test, manufacturing, and optimization. Our ~15,000 employees create world-class solutions in communications, 5G, automotive, energy, quantum, aerospace, defense, and semiconductor markets for customers in over 100 countries. Learn more about what we do.

Our award-winning culture embraces a bold vision of where technology can take us and a passion for tackling challenging problems with industry-first solutions. We believe that when people feel a sense of belonging, they can be more creative, innovative, and thrive at all points in their careers.

We are seeking an Expert Machine Learning Engineer to architect, lead, and scale enterprise-grade machine learning solutions that drive strategic decision-making across Sales, Service, Finance, Order Fulfillment, and Supply Chain. This role demands deep technical expertise in ML/AI, a strong grasp of business operations, and a proven ability to lead cross-functional initiatives from ideation to production. You will serve as a thought leader, mentor, and hands-on contributor, shaping the future of intelligent systems across the organization. 

1. ML Architecture, Strategy & Innovation

• • Define and drive the ML strategy across business domains, identifying high-impact opportunities for automation, optimization, and prediction. 

• • Architect scalable ML systems and reusable frameworks that support real-time inference, batch processing, and continuous learning. 

• • Lead the evaluation and adoption of cutting-edge ML techniques (e.g., foundation models, causal inference, reinforcement learning) to solve complex business problems. 

2. End-to-End ML Lifecycle Ownership

• • Lead the design, development, and deployment of advanced supervised and unsupervised models for use cases such as churn prediction, demand forecasting, fraud detection, and dynamic pricing. 

• • Own the full ML lifecycle: from problem framing and data exploration to model training, validation, deployment, and monitoring. 

• • Champion best practices in experimentation, reproducibility, and responsible AI. 

3. Cross-Functional Leadership & Business Impact

• • Partner with senior stakeholders across Sales, Customer Service, Finance, Supply Chain, and Fulfillment to define and prioritize ML initiatives aligned with strategic goals. 

• • Translate ambiguous business challenges into well-scoped ML solutions with measurable ROI. 

• • Serve as a technical advisor to executive leadership on AI/ML trends, risks, and opportunities. 

4. MLOps, Governance & Infrastructure

• • Lead the design and implementation of robust MLOps pipelines using tools like DataRobot 

• • Ensure scalable, secure, and compliant deployment of models in cloud-native environments (AWS, Azure, GCP). 

• • Establish governance frameworks for model versioning, monitoring, retraining, and auditability. 

5. Data Engineering & Feature Platform Design

• • Collaborate with data engineering teams to define and evolve enterprise-wide feature stores, data contracts, and real-time data pipelines. 

• • Drive innovation in feature engineering, leveraging domain knowledge and advanced statistical techniques. 

6. Mentorship, Collaboration & Thought Leadership

• • Mentor junior ML engineers and data scientists, fostering a culture of technical excellence and continuous learning. 

• • Contribute to internal knowledge sharing, technical design reviews, and ML community engagement. 

• • Publish whitepapers, present at conferences, or lead internal workshops on emerging ML technologies. 

Required: 

• • 8-10  years of experience in machine learning product management, AI engineering, or applied data science, with a strong foundation in software engineering. 

• • Proven experience deploying and scaling ML models in production environments using modern MLOps practices. 

• • Deep understanding of cloud ML platform capabilities (DataRobot is preferred)  

• • Strong communication skills with the ability to influence technical and non-technical stakeholders. 

Preferred: 

• • Experience leading ML initiatives in enterprise domains such as Finance, Sales, or Supply Chain. 

• • Familiarity with advanced ML techniques (e.g., transformers, graph neural networks, time series forecasting, causal modeling). 

• • Exposure to enterprise platforms such as Salesforce, Oracle.  

• • Graduate degree (MS or PhD) in Computer Science, Machine Learning, Statistics, or a related field. 

Careers Privacy Statement***Keysight is an Equal Opportunity Employer.***