Senior Applied Data Scientist - Data Architecture & Feature Engineering

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.

About Keysight AI Labs Keysight’s

AI Labs

is a global R&D group pioneering the integration of

machine learning, generative AI

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.

About the AI Team Join

Keysight's central AI Hub in the heart of Barcelona.

We are expanding our newly formed AI Team.

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.

About the Role 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.

Responsibilities 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 Present results and actionable recommendations to product and R&D stakeholders

Required Qualifications 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

Desired Qualifications Experience with

measurement or simulation-heavy domains

(e.g., wireless, electronics, semiconductor) Familiarity with deep learning frameworks and ML for time-series or unstructured data Visualization skills (e.g., Power BI, Tableau, Plotly) Knowledge of data governance, lineage, metadata management tools Experience with microservices and APIs Open-source contributions or publications

Careers Privacy Statement

***Keysight is an Equal Opportunity Employer.**