Senior Applied Data Scientist - Data Architecture & Feature Engineering at Keysight Technologies, Inc.
Keysight Technologies, Inc. · Barcelona, Spain · Onsite
- Professional
- Office in Barcelona
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 accelerates innovation to connect and secure the world. Our solutions span wireless communications, semiconductors, aerospace & defense, automotive, and beyond. We combine measurement science, simulation, and advanced AI to help engineers design, simulate, and validate the world’s most advanced systems.
About the AI Team
Keysight AI Team pioneers scientific and secure AI — including physics-informed learning, test-and-measurement-augmented intelligence, and trustworthy ML for mission-critical industries. We collaborate across research, product and engineering teams to deliver cutting-edge AI capabilities baked directly into Keysight’s products.
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
Qualifications:
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.***
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