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Machine Learning Quantum Computing Jobs in Columbus, OH

Python Tutor

Columbus, OH · Remote

$40/hr

Emphasizes readable, maintainable code and connects Python to machine learning, web scraping, scientific computing, and DevOps applications. * Curriculum Awareness & Adaptive Instruction: Familiar ...

Cyber Data Protection/PKI Manager

Columbus, OH · Hybrid

$107.20K - $144.90K/yr

... post-quantum cryptography, confidential computing, secure enclaves, envelope encryption) and ... trust, and machine identity management Experience with DevSecOps integration, CI/CD pipeline ...

Enthusiasm for keeping current with advances in AI and machine learning, with the ability to ... from high-performance computing, artificial intelligence (AI), and advanced electronics to ...

Our teams work with complex global datasets, AI and machine learning, hybrid cloud solutions, and ... Experience with cloud-based computing and internet issues preferred Working Conditions: Normal ...

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Machine Learning Quantum Computing information

See Columbus, OH salary details

$24.6K

$41.1K

$85K

How much do machine learning quantum computing jobs pay per year?

As of May 28, 2026, the average yearly pay for machine learning quantum computing in Columbus, OH is $41,131.00, according to ZipRecruiter salary data. Most workers in this role earn between $31,400.00 and $44,400.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Machine Learning Quantum Computing Specialist, and why are they important?

To thrive in Machine Learning Quantum Computing, you need strong foundations in quantum mechanics, linear algebra, and advanced machine learning concepts, typically supported by a degree in physics, computer science, or a related field. Familiarity with quantum programming languages (such as Qiskit or Cirq), cloud-based quantum platforms, and proficiency in Python are usually required, alongside experience with relevant certifications or coursework. Strong problem-solving skills, adaptability, and effective collaboration are vital soft skills in this interdisciplinary field. These competencies are crucial for driving innovation and bridging the gap between quantum computing and practical machine learning applications.

How do professionals in Machine Learning Quantum Computing typically collaborate with interdisciplinary teams?

Professionals in Machine Learning Quantum Computing often work closely with experts in physics, computer science, and engineering. Collaboration usually involves translating quantum concepts for machine learning specialists and vice versa, ensuring that algorithms are both theoretically sound and practically implementable on quantum hardware. Regular meetings, code reviews, and knowledge-sharing sessions are standard, as interdisciplinary insight is crucial for advancing research and developing scalable solutions. Effective communication and a willingness to learn from other domains are essential for success in these teams.

What is Machine Learning Quantum Computing?

Machine Learning Quantum Computing is an interdisciplinary field that combines principles of quantum computing with machine learning techniques. It aims to leverage the computational power of quantum computers to enhance the performance of machine learning algorithms, potentially solving complex problems more efficiently than classical computers. This area includes developing quantum algorithms for tasks such as classification, clustering, and optimization, as well as using machine learning to improve quantum hardware and error correction. Researchers expect that, as quantum hardware matures, this field could revolutionize data analysis, cryptography, and scientific discovery.

What is the difference between Machine Learning Quantum Computing vs Data Scientist?

AspectMachine Learning Quantum ComputingData Scientist
Required CredentialsAdvanced degrees in quantum computing, machine learning, or related fieldsDegree in data science, statistics, or computer science
Work EnvironmentResearch labs, tech companies focusing on quantum tech, academiaBusiness environments, tech companies, consulting firms
Industry UsageEmerging quantum tech industry, research institutionsFinance, healthcare, marketing, e-commerce
Common Search/ComparisonQuantum algorithms, quantum machine learningData analysis, predictive modeling

Machine Learning Quantum Computing specialists focus on developing algorithms that leverage quantum mechanics to enhance machine learning tasks, often requiring advanced knowledge of quantum physics. Data Scientists analyze and interpret large datasets using traditional machine learning techniques. While both roles involve machine learning, the former emphasizes quantum computing applications, whereas the latter centers on data analysis in conventional computing environments.

What job categories do people searching Machine Learning Quantum Computing jobs in Columbus, OH look for? The top searched job categories for Machine Learning Quantum Computing jobs in Columbus, OH are:
Applied AI/ML Modeling - Vice President

Applied AI/ML Modeling - Vice President

Chase

Columbus, OH • On-site

Other

Medical, Retirement

This job post has expired 1 day ago. Applications are no longer accepted.


JPMorgan Chase & Co. rating

8.1

Company rating: 8.1 out of 10

Based on 466 frontline employees who took The Breakroom Quiz

46th of 141 rated banks


Job description

Applied AI Modeling Vice President

Our Branch Network Modeling team develops advanced analytics and machine learning solutions that inform high-impact decisions across physical location strategy and field workforce effectiveness.

As an Applied AI Modeling Vice President in Branch Network Modeling team, you will build advanced artificial intelligence (AI) and machine learning (ML) models that directly shape high-stakes decisions impacting Chase's branch network and the bankers who serve our customers. Your models will help optimize our branch network, using geospatial AI and graph-based models to determine where Chase should invest, grow, or reposition its physical footprint, or will empower our bankers in the field to serve our customers using techniques like reinforcement learning and behavioral science.

Job responsibilities

  • Develop and launch AI and ML models that solve complex, ambiguous business problems in Consumer Banking, spanning areas such as retail network optimization, investment optimization, resource allocation, and sales effectiveness.
  • Lead modeling engagements end-to-end, including interfacing with business, governance, UX, and technology stakeholders; articulating clear business use cases; delivering on project plans; and working with large, complex datasets — including geospatial, demographic, transactional, and behavioral data — to formulate testable business hypotheses.
  • Translate technical model outputs into clear, actionable recommendations for non-technical business partners in Real Estate, Finance, and Market Strategy.
  • Partner with governance teams to expedite fair and thorough model reviews, track performance metrics, and maintain adherence to regulatory compliance standards.

Required qualifications, capabilities, and skills

  • Advanced degree (master's or PhD) in a quantitative or spatial discipline such as Computer Science, Statistics, Machine Learning, Operations Research, Applied Mathematics, or Geography, or a related field.
  • 4+ years of hands-on, relevant industry experience in developing and deploying AI/ML models, including statistical modeling, ML, reinforcement learning, or optimization algorithms.
  • Proficient in Python with hands-on experience in ML and deep learning frameworks (TensorFlow, PyTorch) and libraries (e.g., NumPy, Scikit-Learn, Pandas). Strong working knowledge of Jupyter Notebook/Lab and cloud computing.
  • Deep expertise in at least one of the following, with meaningful exposure to at least one other:
    • Geospatial analytics, spatial statistics, or spatial optimization
    • Graph neural networks, network science, or graph-based optimization
    • Reinforcement learning, multi-armed bandits, or online/continuous learning
    • Behavioral modeling, adaptive intervention design, or human performance optimization

Preferred qualifications, capabilities, and skills

  • Hold a PhD in a relevant discipline.
  • Experience developing advanced AI or ML models in consumer finance, logistics, major retailers, or AI-native platforms.
  • Experience with at least one of the following: geospatial tools and libraries (e.g., GeoPandas, PySAL, H3, Esri/ArcGIS, Carto, Wherobots, QGIS), graph ML frameworks (e.g., PyTorch Geometric, DGL, NetworkX), RL libraries (e.g., RLlib, Stable Baselines, Vowpal Wabbit).
  • Familiarity with behavioral science concepts (e.g., nudge theory, decision theory) or experience building adaptive, continuous learning, or recommendation systems.
  • Experience with Databricks, Snowflake, or similar platforms.
About Us

Chase is a leading financial services firm, helping nearly half of America's households and small businesses achieve their financial goals through a broad range of financial products. Our mission is to create engaged, lifelong relationships and put our customers at the heart of everything we do. We also help small businesses, nonprofits and cities grow, delivering solutions to solve all their financial needs.

We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.

Equal Opportunity Employer/Disability/Veterans

About the Team

Our Consumer & Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We're proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions – all while ranking first in customer satisfaction. The CCB Data & Analytics team responsibly leverages data across Chase to build competitive advantages for the businesses while providing value and protection for customers. The team encompasses a variety of disciplines from data governance and strategy to reporting, data science and machine learning. We have a strong partnership with Technology, which provides cutting edge data and analytics infrastructure. The team powers Chase with insights to create the best customer and business outcomes.


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