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Phd Optimization Research Jobs (NOW HIRING)

$120 - $220/hr

This role is designed for new PhD graduates or early‑career researchers interested in applying ... than optimization of new machine learning methodologies. This role can be remote within the U.S ...

New

... and optimization of therapeutic screening strategies. At Children's Mercy, we are committed to ... PHD or MD and 3-5 years of experience Benefits at Children's Mercy The benefits plans at Children ...

... and optimization of therapeutic screening strategies. At Children's Mercy, we are committed to ... PHD or MD and 3-5 years of experience Benefits at Children's Mercy The benefits plans at Children ...

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Phd Optimization Research information

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$29.5K

$95.3K

$155K

How much do phd optimization research jobs pay per year?

As of Aug 20, 2026, the average yearly pay for phd optimization research in the United States is $95,315.00, according to ZipRecruiter salary data. Most workers in this role earn between $79,000.00 and $109,000.00 per year, depending on experience, location, and employer.

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 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 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 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.

More about Phd Optimization Research jobs

What cities are hiring for Phd Optimization Research jobs?

Cities with the most Phd Optimization Research job openings:

What states have the most Phd Optimization Research jobs?

States with the most job openings for Phd Optimization Research jobs include:

Infographic showing various Phd Optimization Research job openings in the United States as of August 2026, with employment types broken down into 1% Internship, 1% As Needed, 85% Full Time, 11% Part Time, and 2% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution, with an average salary of $95,315 per year, or $45.8 per hour.

Research Scientist, PhD New Grad

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On-site

$120 - $220/hr

Other

Medical, Retirement, PTO

Posted 2 days ago

New


Job description

SentiLink provides innovative identity and risk solutions, empowering institutions and individuals to transact with confidence. We’re building the future of identity verification in the United States replacing a clunky, ineffective, and expensive status quo with solutions that are 10x faster, smarter, and more accurate.

We’ve seen tremendous traction and are growing extremely quickly. Our real‑time APIs have helped verify hundreds of millions of identities, starting with financial services and rapidly expanding into new markets. SentiLink is backed by world‑class investors including Craft Ventures, Andreessen Horowitz, NYCA, and Max Levchin.

We’ve earned recognition from TechCrunch, CNBC, Bloomberg, Forbes, Business Insider, PYMNTS, American Banker, LendIt, and have been named to the Forbes Fintech 50. We have also been named a 2026 FICO Industry Vanguard Decision Award Winner. Last but not least, we’ve even made history - we were the first company to go live with the eCBSV and testified before the United States House of Representatives on the future of identity.

SentiLink supports a variety of ways to work, ranging from fully remote to in‑office. We operate as a digital‑first company with strong collaboration across the U.S. and India. We maintain physical offices in Austin, San Francisco, New York City, Seattle, Los Angeles, and Chicago in the U.S., and in Gurugram (Delhi) and Bengaluru in India. If you’re located near one of these offices, we would love for you to spend time in the office regularly. Some roles are hybrid or in‑office by design. For example, our engineering team in India works primarily from our Gurugram office.

Role:

As a Research Scientist at SentiLink, you will build our core products: models that identify fraudsters and also advance our growing suite of products in financial risk. This role is designed for new PhD graduates or early‑career researchers interested in applying machine learning to real‑world fraud detection. You’ll build and ship machine learning models in a production environment, gaining hands‑on experience across the full ML lifecycle, from research and development to deployment at scale. If you're looking for real‑world AI and ML exposure in an industry setting, not just research papers, this is it.

We have open roles on multiple teams including:

  • Emerging Products - focuses on 0‑to‑1 development of new offerings brought to market
  • Application Fraud - analyzes the foundational elements of consumer financial applications to detect all forms of fraud
  • Identity - resolves identities across massive, often conflicting data sources (both digital and physical) and generates risk models from limited information

You will be relied upon to be technically capable and the definitive owner of your respective domain. You will often work on projects with high visibility and impact that require deep domain understanding, critical thinking and strong technical abilities. You will work with teams across the company to research new types of fraud, develop new products, and provide analysis to drive sales and marketing. This is a full‑stack data science role, involving model development, analysis, and writing production code. You should be interested in having end‑to‑end ownership and a fast‑moving environment where deep domain understanding drives development and unusual insights drive our competitive advantage rather than optimization of new machine learning methodologies.

This role can be remote within the U.S., with a strong preference for candidates who can work from our Austin, San Francisco, or New York offices.

Technologies: Python 3, PostgreSQL, and AWS infrastructure (EC2, S3, RDS, Redshift, etc.)

Responsibilities:
  • Develop and maintain SentiLink’s fraud detection models through the full model development lifespan: from data acquisition decisions through featurization, focusing labeling resources, model training, experimentation, productionalization, and monitoring.
  • Build foundational modeling to drive SentiLink’s expanding suite of Fraud and Financial Risk products.
  • Research new types of fraud and develop new SentiLink products around identity verification.
  • Achieve success by researching / developing through iteration, integration of new data sources and inventive feature engineering.
  • Write production‑ready code that can be relied on for real‑time decision making by our partners.
  • Design, perform, and present analyses that will inform data acquisition, product development, risk operations priorities, marketing, and sales efforts.
  • Work with engineering, risk operations, and data acquisitions to access necessary data, maintain data quality, and support data access
Requirements:
  • Bachelor’s, Master’s, or PhD in Statistics, Computer Science, Physics, Mathematics, or a related quantitative field or equivalent experience/research
  • Strong foundation in machine learning, statistics, or applied data science
  • Experience with Python and common data science tools through coursework, research, internships, or personal projects
  • Demonstrated ability to analyze complex problems and build data‑driven solutions
  • Strong communication skills and ability to explain technical ideas clearly
  • Interest in learning deeply about fraud, identity, and financial risk systems
  • Ability to write clean, maintainable code
  • Strong attention to detail and curiosity about real‑world data problems
  • Candidates must be legally authorized to work in the United States and must live in the United States
  • Thrive in a fast paced environment characterized by the need to solve extremely varied, high impact, open ended problems
Salary Range:

$120,000/year - $220,000/year + equity + benefits

Note: This salary range is inclusive of multiple career levels, and the actual base salary within that range will be determined by several components including but not limited to the individual's education, experience, skills, and qualifications.

Perks:
  • Employer paid group health insurance for you and your dependents
  • 401(k) plan with employer match (or equivalent for non US-based roles)
  • Flexible paid time off
  • Regular company‑wide in‑person events
  • Home office stipend, and more!
Corporate Values:
  • Follow Through
  • Deep Understanding
  • Whatever It Takes
  • Do Something Smart
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