Applied Data Scientist

Keysight Technologies, Inc.•Loveland, OH

About The Position

As an Applied Data Scientist, you’ll operate at the intersection of data engineering, data science, and machine learning. You’ll design and implement large-scale data architectures, develop robust data pipelines, and build high-quality ML models that integrate simulation and measurement data from diverse domains. Your work will directly influence Keysight’s advanced R&D initiatives, from algorithm development to AI-assisted engineering tools.

Requirements

Master’s or PhD in Data Science, Computer Science, Electrical Engineering, Statistics, or related field.
1+ years’ experience as a Data Scientist / Applied Data Scientist
Proven ability to build and maintain scalable data infrastructures (data lakes, schemas, pipelines).
Strong programming skills in Python (pandas, numpy, scikit-learn), SQL, and optionally C++.
Proficiency with Snowflake, Databricks, or similar big-data environments.
Hands-on expertise in tree-based ML techniques and statistical modeling.
Familiarity with Bayesian Optimization and feature engineering for time-series or signal data.
Ability to move fluidly between data exploration, engineering, and modeling tasks.

Nice To Haves

Experience in data architecture design, schema governance, or cross-team data standards.
Knowledge of MLOps principles for productionizing models and maintaining pipelines.
Experience with metadata management and feature store design.
Prior exposure to environments combining simulation and real-world measurement data.

Responsibilities

Partner with internal engineering and data teams to identify key data sources, define feature requirements, and align data standards across organizations.
Design, implement, and maintain data lakes, databases, and ETL/ELT pipelines (Snowflake, Databricks, SQL, Python).
Integrate, clean, and align simulation, measurement, and operational data for scalable AI/ML model development.
Conduct exploratory data analysis, dimensionality reduction (e.g., PCA), clustering, and regression to extract insights.
Develop and validate ML models using tree-based methods (XGBoost, LightGBM, Random Forests) and Bayesian Optimization for tuning.
Apply signal processing and data augmentation techniques to improve data quality and coverage.
Document data lineage, feature definitions, and modeling rationale for reproducibility and transparency.
Communicate insights and recommendations to stakeholders, influencing data-driven decisions across R&D and product teams.