Senior Applied Data Scientist - Data Architecture & Feature Engineering
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Job description
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 LabsKeysight'sAI Labsis a global R&D group pioneering the integration ofmachine learning, generative AIinto 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 TeamJoin 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 RoleWe are seeking aSenior Applied Data Scientistwith strongdata engineeringcapabilities.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.ResponsibilitiesPartner with internal experts to identify critical data sources and define ML-relevant featuresArchitect and build scalable data lakes/databases for standardized and efficient cross-org data accessClean, align, normalize, and integrate data from simulations, measurements, and operational systemsDevelop and maintain reproducible ETL/ELT pipelines for structured and unstructured data using SQL, Python, Snowflake, and cloud-native workflowsPerform EDA, feature engineering, regression, and dimensionality reduction to generate high-value insightsEnsure data governance, lineage, metadata management, and complianceSupport experiment design, hypothesis testing, and statistical modelingWork closely with ML engineers to accelerate model training, deployment, and ongoing monitoringPresent results and actionable recommendations to product and R&D stakeholdersRequired QualificationsMaster's in Data Science, Statistics, CS, EE, or related quantitative field5+ years of experience as an applied data scientist or hybrid DS/DE roleExpert proficiency inPython, SQL, and data manipulation librariesStrong background in statistics, algorithms, and data structuresExperience with relational + NoSQL databases and designing scalable data architecturesHands-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 / GCPClear track record of driving data-to-value outcomesDesired QualificationsExperience withmeasurement or simulation-heavy domains(e.g., wireless, electronics, semiconductor)Familiarity with deep learning frameworks and ML for time-series or unstructured dataVisualization skills (e.g., Power BI, Tableau, Plotly)Knowledge of data governance, lineage, metadata management toolsExperience with microservices and APIsOpen-source contributions or publicationsCareers Privacy StatementKeysight is an Equal Opportunity Employer.#J-*****-Ljbffr
Requirements
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, 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