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Mathematical Optimization Remote Jobs in Austin, TX

Targeted Talent

Senior Deep Learning Engineer

Austin, TX · On-site +1

$130K - $180K/yr

We're seeking top-notch engineers to join our team. As part of our group, you'll collaborate with hardware and software engineers to design, develop, and optimize software for our chip, making AI

Adidev Technologies Inc

DATA SCIENTIST

Austin, TX · Remote

Adidev Technologies Inc www.adidevtechnologies.com URGENT HIRE - HIRING PROCESS - 24-48 HOURS! Adidev Technologies is seeking 1-2 yrs of relevant experience in Data Science. A project can last

Dlapiper

Data Scientist

Austin, TX · On-site +1

DLA Piper is, at its core, bold, exceptional, collaborative and supportive. Our people are the backbone, heart and soul of our firm. Wherever you are in your professional journey, DLA Piper is a

SpyCloud

Senior Data Scientist, Applied ML

Austin, TX · On-site +1

$154K - $200K/yr

SpyCloud is on a mission to make the internet a safer place by disrupting the criminal underground. SpyCloud's solutions thwart cyberattacks and protect more than 4 billion accounts worldwide.

All JobsMathematical Optimization JobsMathematical Optimization Remote JobsMathematical Optimization Remote Jobs in Austin, TX

Mathematical Optimization Remote information

See Austin, TX salary details

$82.8K

$125.9K

$169.5K

How much do mathematical optimization remote jobs pay per year?

As of Sep 12, 2026, the average yearly pay for mathematical optimization remote in Austin, TX is $125,914.00, according to ZipRecruiter salary data. Most workers in this role earn between $108,000.00 and $142,200.00 per year, depending on experience, location, and employer.

What is a mathematical optimization remote job?

A Mathematical Optimization Remote job involves using mathematical techniques and algorithms to solve optimization problems, such as maximizing efficiency or minimizing costs, while working from a remote location. Professionals in this field apply optimization theory, modeling, and computational methods to real-world problems in industries like logistics, finance, engineering, and data science. Remote roles allow for flexibility, enabling collaboration with teams and clients online while leveraging specialized software and programming languages such as Python, MATLAB, or R.

What are the key skills and qualifications needed to thrive as a mathematical optimization specialist working remotely?

To thrive as a Mathematical Optimization Specialist in a remote setting, you need a strong background in mathematics, operations research, or computer science, often supported by an advanced degree. Proficiency with optimization software (such as Gurobi, CPLEX, or MATLAB), programming languages like Python or R, and familiarity with cloud-based collaboration tools is typically required. Excellent problem-solving abilities, self-motivation, and clear communication skills help you stand out when collaborating with distributed teams and stakeholders. These skills and qualities are crucial for efficiently developing, implementing, and explaining optimization solutions in a remote work environment.

What are some common challenges faced by professionals in remote mathematical optimization roles, and how can they be addressed?

Remote mathematical optimization professionals often encounter challenges such as limited real-time collaboration with team members, managing complex problem-solving tasks independently, and ensuring effective communication of technical findings to non-technical stakeholders. To address these challenges, it's helpful to establish regular virtual meetings, use collaborative tools for sharing code and results, and develop clear documentation. Additionally, proactively seeking feedback and staying engaged with the broader team can help maintain alignment and foster innovation.

What is the difference between Mathematical Optimization Remote vs Data Analyst Remote?

AspectMathematical Optimization RemoteData Analyst Remote
Required CredentialsDegree in Mathematics, Operations Research, or related field; proficiency in optimization softwareDegree in Statistics, Mathematics, or related field; proficiency in data analysis tools
Work EnvironmentRemote, often collaborative with teams on complex modeling projectsRemote, focused on data collection, visualization, and reporting
Industry UsageFinance, logistics, supply chain, tech companiesMarketing, finance, healthcare, tech companies
Common Search/ComparisonYesNo

Mathematical Optimization Remote specialists focus on developing algorithms to optimize processes and decision-making, often requiring advanced mathematical skills. Data Analysts Remote interpret data to provide insights, using statistical tools. While both roles are remote and involve data, they differ in technical focus and industry applications.

What are popular job titles related to Mathematical Optimization Remote jobs in Austin, TX?

For Mathematical Optimization Remote jobs in Austin, TX, the most frequently searched job titles are:

What job categories do people searching Mathematical Optimization Remote jobs in Austin, TX look for?

The top searched job categories for Mathematical Optimization Remote jobs in Austin, TX are:

What cities near Austin, TX are hiring for Mathematical Optimization Remote jobs?

Cities near Austin, TX with the most Mathematical Optimization Remote job openings:

Mathematical Optimization Remote jobs near you
Infographic showing various Mathematical Optimization Remote job openings in Austin, TX as of September 2026, with employment types broken down into 1% Internship, 85% Full Time, 12% Part Time, and 2% Contract. Highlights an 77% Physical, 5% Hybrid, and 18% Remote job distribution, with an average salary of $125,914 per year, or $60.5 per hour.

R&D Data Scientist: Mathematical Modeling and Optimization

Austin, TX • On-site, Remote

Liftlab Analytics, Inc.
See more jobs at Liftlab Analytics, Inc.external

Full-time

Re-posted 24 days ago

Job description

(Fully-remote US position)
About LiftLab
Liftlab is the leading provider of science-driven software to optimize marketing spend and predict revenue for optimal spend levels. We call this the Science of Marketing Effectiveness. Our platform combines economic modeling with specialized media experimentation so brands and agencies can clearly see the tradeoffs of growth and profitability. With decades of experience in marketing analytics and data science, our team of industry experts and thought leaders is proud to enable leading and emerging brands such as Cinemark, Express, Hanna Anderson, Lulu & Georgia, Pandora, Sephora, Skims, Tory Burch, Thrive, and Vionic, with our cutting-edge solutions and strategic guidance.
Job responsibilities
  • Develop new algorithm-based features of LiftLab's marketing measurement and optimization platform
  • Performs diagnostics and root-cause analysis and provide fixes
  • Works with Data Science and Engineering to implement these features into LiftLabs product and workflow
Course work/experience:
  • Data manipulation
    • SQL
    • Operating on big datasets in Python
    • Data visualization
  • Mathematical optimization
    • Linear optimization concepts
    • Nonlinear continuous optimization
    • Linear algebra
  • Mathematical modeling
    • Using parametrized systems of equations to represent real-world systems
  • Statistics
    • Multivariate regression
    • Clear understanding of Maximum Likelihood estimation and computational methods to find MLE parameters
    • Bayesian concepts
    • Hypotheses testing
Education requirements
Graduate degree in Applied Mathematics, Scientific Computing, Operations Research or related field. We will consider holders of Bachelor degrees with relevant experience
Skills/Aptitude
  • Engineering and detective mindset
    • Both to diagnose data and existing algorithms and to develop new analytics functionality
  • Pragmatic approach to real-world problems
  • Focus on problem solving over applying specific models
  • Willingness to make approximations and assumptions rather than find "the" optimal solution
  • Ability to combine multiple techniques and models to solve end-to end-problems
  • Communication and collaboration skill
  • Ability to convert non-technical requests into project specifications

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