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Phd Optimization Research Jobs in Utah (NOW HIRING)

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Senior Chemist

Salt Lake City, UT

$110K - $120K/yr

... optimization of chemical products * Design and execute experiments to support R&D initiatives and ... Bachelor's degree in Chemistry or related field (Master's or PhD preferred) * 5-10+ years of ...

Myriad Genetics

Senior Product Manager, MRD

Salt Lake City, UT

$121.40K - $160.20K/yr

Coordinate closely with R&D, clinical, regulatory, medical affairs, and commercial teams to ensure ... Track product performance, gather customer and field feedback, and drive continuous optimization to ...

DataAnnotation

Decision Scientist

Salt Lake City, UT · On-site +1

$40/hr

Qualifications 2+ years of hands‐on experience in a quantitative role or research environment ... PhD is a plus. Relevant credentials are a plus (e.g., Kaggle Competition ranking, AWS/GCP ML ...

DataAnnotation

Physicist - AI Trainer

Salt Lake City, UT · On-site +1

$40/hr

Qualifications 2+ years of hands‐on experience in a quantitative role or research environment ... PhD is a plus. Relevant credentials are a plus (e.g., Kaggle Competition ranking, AWS/GCP ML ...

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Phd Optimization Research information

What are the key skills and qualifications needed to thrive as a PhD Optimization Researcher, and why are they important?

To excel as a PhD Optimization Researcher, you typically need a doctorate in applied mathematics, computer science, operations research, or a related field, along with expertise in mathematical modeling and algorithm development. Proficiency with programming languages such as Python, MATLAB, or C++, and familiarity with optimization libraries and tools like Gurobi or CPLEX are commonly required. Strong analytical thinking, creativity, and effective communication skills help in formulating novel solutions and collaborating with interdisciplinary teams. These competencies are crucial for advancing research, solving complex optimization problems, and effectively disseminating findings within both academic and industry settings.

What are the typical collaborative projects that a PhD Optimization Researcher might work on within a multidisciplinary team?

PhD Optimization Researchers often collaborate on projects that integrate expertise from fields such as data science, engineering, computer science, and business analytics. These projects may involve developing and implementing advanced optimization algorithms to solve complex, real-world problems like supply chain management, resource allocation, or energy systems modeling. Team members typically contribute domain knowledge, data, and problem requirements, while the optimization researcher focuses on model formulation, algorithm selection, and solution analysis. Effective communication and adaptability are essential, as researchers must translate technical findings into actionable insights for stakeholders.

What is a PhD in Optimization Research?

A PhD in Optimization Research is an advanced academic degree focused on developing and analyzing mathematical models and algorithms to find the best possible solutions to complex problems. This field often involves linear and nonlinear programming, combinatorial optimization, and stochastic processes, and is applied in areas such as operations research, machine learning, logistics, and engineering. Graduates are prepared for careers in academia, industry, or research institutions, where they work on improving decision-making processes and resource allocation. The program typically involves coursework, comprehensive exams, and original research leading to a dissertation.

What is the difference between Phd Optimization Research vs Data Scientist?

AspectPhd Optimization ResearchData Scientist
Required CredentialsPhD in Operations Research, Applied Mathematics, or related fieldBachelor's or Master's in Data Science, Computer Science, or related field; some roles prefer PhD
Work EnvironmentResearch labs, academia, R&D departments in industryTech companies, finance, healthcare, consulting firms
Industry UsageFocus on developing optimization algorithms, mathematical modelingFocus on data analysis, machine learning, predictive modeling
Common Search/ComparisonYesYes

While both roles involve advanced analytical skills, Phd Optimization Research primarily focuses on developing and refining optimization algorithms and mathematical models, often in research or academic settings. Data Scientists analyze large datasets to extract insights and build predictive models, often applying machine learning techniques. The roles overlap in data analysis and quantitative skills but differ in their core focus and typical work environments.

What are popular job titles related to Phd Optimization Research jobs in Utah? For Phd Optimization Research jobs in Utah, the most frequently searched job titles are:
What job categories do people searching Phd Optimization Research jobs in Utah look for? The top searched job categories for Phd Optimization Research jobs in Utah are:
What cities in Utah are hiring for Phd Optimization Research jobs? Cities in Utah with the most Phd Optimization Research job openings:
Phd Optimization Research jobs near you
Operations Research Analyst - AI Trainer

Operations Research Analyst - AI Trainer

DataAnnotation

Salt Lake City, UT • On-site, Remote

$40/hr

Full-time

This job post has expired today. Applications are no longer accepted.

Job description

Join the DataAnnotation team and contribute to developing cutting-edge AI systems, while enjoying the flexibility of remote work and setting your own schedule. We are looking for experienced quantitative professionals to help advance AI development. AI models are increasingly capable of performing complex analytical and scientific reasoning — but these systems still need practitioners with real-world quantitative experience to validate whether the outputs actually hold up in practice.

That's where you come in. As a member of DataAnnotation's team, you'll work closely with state-of-the-art AI models on tasks like evaluating AI-generated quantitative analysis, solving technical problems, and providing feedback that directly shapes how these systems reason about data, models, and scientific problems. Whether your background is in data science, astrophysics, economics, biostatistics, operations research, or any other quantitative field, if you think rigorously about data and models, your skills are directly applicable here.

Some team members fit this work alongside a full-time role, while others treat it as their primary focus. To get started, once you sign up for an account, you'll take a short assessment (this serves as our version of an interview). If you pass, you'll receive an email confirmation, and paid work will become available on our platform.

Benefits Fully remote: work from anywhere in the US, Canada, UK, Ireland, Australia, and New Zealand. Flexible schedule: choose which projects you take on and when you work. Competitive pay: projects are paid hourly, starting at $40+ USD per hour.

Impact: help shape the future of AI systems built to reason about data and analytics. Responsibilities Evaluate AI-generated quantitative work, including statistical analysis, predictive modeling, scientific reasoning, and data-driven insights, for technical accuracy and real-world validity. Design and solve quantitative problems used to train and benchmark AI systems, spanning areas like forecasting, experimental analysis, optimization, and statistical inference.

Write clear technical explanations and well-documented analytical code. Provide feedback that directly shapes the next generation of AI models built for quantitative reasoning. Qualifications 2+ years of hands‐on experience in a quantitative role or research environment — such as data science, statistics, economics, finance, physics, biology, epidemiology, operations research, or any adjacent field.

Some coding experience required, with comfort writing and reviewing analytical code end-to-end. Practical experience with statistical methods, predictive modeling, and experiment design (e.g., A/B testing, hypothesis testing, regression, classification, time‐series forecasting). Fluency in English (native or bilingual level) with strong writing skills.

A bachelor's degree in a quantitative field is preferred (Statistics, Computer Science, Mathematics, Engineering, or similar); a master's or PhD is a plus. Relevant credentials are a plus (e.g., Kaggle Competition ranking, AWS/GCP ML certifications, or equivalent demonstrated expertise). Note: Payment is made via PayPal.

We will never ask for any money from you. This job is only available to those in the US, Canada, UK, Ireland, Australia, and New Zealand. #datascience #J-18808-Ljbffr

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