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How much do quantum data scientist jobs pay per year?

As of Jul 5, 2026, the average yearly pay for quantum data scientist in the United States is $165,018.00, according to ZipRecruiter salary data. Most workers in this role earn between $133,500.00 and $170,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Quantum Data Scientist, and why are they important?

To thrive as a Quantum Data Scientist, you need a strong background in quantum physics, machine learning, and advanced mathematics, typically supported by a degree in physics, computer science, or a related field. Familiarity with quantum programming languages (such as Qiskit or Cirq), classical data science tools (like Python, TensorFlow, or PyTorch), and experience using quantum computing platforms are essential. Strong analytical thinking, problem-solving abilities, and effective communication skills help you translate complex quantum concepts and collaborate with interdisciplinary teams. These skills are crucial for driving innovation and developing practical solutions in the rapidly evolving field of quantum data science.

What is a Quantum Data Scientist?

A Quantum Data Scientist is a professional who combines expertise in quantum computing and data science to develop algorithms and models that leverage the power of quantum computers. They work on solving complex data problems that are difficult or impossible for classical computers to handle, using quantum algorithms and techniques. Their role often includes research, algorithm development, data analysis, and collaboration with other scientists and engineers to advance quantum technologies. Quantum Data Scientists typically have a background in physics, computer science, mathematics, and machine learning, and they play a key role in the emerging field of quantum information science.

How does a Quantum Data Scientist typically collaborate with classical data science and engineering teams?

Quantum Data Scientists frequently work alongside classical data scientists and data engineers to identify problems that could benefit from quantum computing techniques. Collaboration often involves translating complex business or scientific challenges into quantum algorithms, benchmarking quantum approaches against classical solutions, and integrating quantum models into existing workflows. Effective communication is key, as quantum concepts may be unfamiliar to traditional teams, so Quantum Data Scientists often act as bridges, sharing insights and training colleagues on quantum methodologies. This collaborative approach accelerates innovation and helps organizations assess the real-world value of quantum solutions.
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Quantum Data Scientist jobs near you
Infographic showing various Quantum Data Scientist job openings in the United States as of June 2026, with employment types broken down into 83% Full Time, and 17% Temporary. Highlights an 100% In-person job distribution, with an average salary of $165,018 per year, or $79.3 per hour.
Senior Applied Data Scientist - Data Architecture & Feature Engineering

Senior Applied Data Scientist - Data Architecture & Feature Engineering

Keysight Technologies, Inc.

Harrisonville, NJ • On-site

Other

Posted 9 days ago

Keysight Technologies rating

8.1

Company rating: 8.1 out of 10

Based on 20 frontline employees who took The Breakroom Quiz

41st of 141 rated electronics manufacturers

Job description

Overview

Keysightis 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 moreabout what we do. 

Our award-winningculture 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

Careers Privacy Statement***Keysight is an Equal Opportunity Employer.*** 

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.*** 

Education:UNAVAILABLEEmployment Type: UNAVAILABLE

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