Senior Applied Data Scientist - Data Architecture & Feature Engineering

Overview:

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

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