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. Our culture embraces a bold vision of where technology can take us and a passion for tackling challenging problems with industry-first solutions. About Keysight AI Labs
Keysight’s AI Labs is a global R&D group pioneering the integration of into Keysight’s test, measurement, and design solutions. Our mission is to transform how engineers design, simulate, and validate advanced systems- from 6G and semiconductors to quantum and automotive - by embedding AI throughout our workflows.
As part of this growing team, you will join a vibrant, cross-functional environment that brings together experts in ML engineering, data science, physics-informed modeling, and software development. You’ll work closely with domain experts across RF, EM, circuit design, and test & measurement to accelerate scientific innovation through AI.
We are seeking a Senior Applied Data Scientist with strong data engineering capabilities. You will explore complex engineering data, architect scalable data infrastructure, and shape the data foundation powering AI model development across Keysight products. This role bridges research and production, from data discovery to robust ETL/ELT pipeline design and feature creation for ML models.
Partner with internal experts to identify critical data sources and define ML-relevant features
Architect and build scalable data lakes/databases for standardized and efficient cross-org data access
Clean, align, normalize, and integrate data from simulations, measurements, and operational systems
Develop and maintain reproducible ETL/ELT pipelines for structured and unstructured data using SQL, Python, Snowflake, and cloud-native workflows
Perform EDA, feature engineering, regression, and dimensionality reduction to generate high-value insights
Ensure data governance, lineage, metadata management, and compliance
Support experiment design, hypothesis testing, and statistical modeling
Work closely with ML engineers to accelerate model training, deployment, and ongoing monitoring
Master’s in Data Science, Statistics, CS, EE, or related quantitative field
~5+ years of experience as an applied data scientist or hybrid DS/DE role
~ Expert proficiency in Python, SQL, and data manipulation libraries
~ Strong background in statistics, algorithms, and data structures
~ Experience with relational + NoSQL databases and designing scalable data architectures
~ Hands-on experience with big data tools (e.g., Spark, Kafka, Snowflake, Databricks, Hadoop )
~ Experience supporting ML workflows — MLOps, CI/CD, containerization (Docker/Kubernetes)
~ Experience with cloud platforms: Azure / AWS / GCP
~ Clear track record of driving data-to-value outcomes
wireless, electronics, semiconductor)
Familiarity with deep learning frameworks and ML for time-series or unstructured data
Power BI, Tableau, Plotly)
Knowledge of data governance, lineage, metadata management tools