Senior Staff AI Scientist
Atlanta, GA · On-site
Proficient in multiple optimization paradigms such as combinatorial optimization, gradient methods, or Bayesian optimization. * Proficient in NLP techniques, Explainable AI, and ML frameworks.
Senior Staff AI Scientist
Atlanta, GA · On-site
Proficient in multiple optimization paradigms such as combinatorial optimization, gradient methods, or Bayesian optimization. * Proficient in NLP techniques, Explainable AI, and ML frameworks.
Staff AI Scientist
Atlanta, GA · On-site
Proficient in multiple optimization paradigms such as combinatorial optimization, gradient methods, or Bayesian optimization. * Proficient in NLP techniques, Explainable AI, and ML frameworks.
Staff AI Scientist
Atlanta, GA · On-site
Proficient in multiple optimization paradigms such as combinatorial optimization, gradient methods, or Bayesian optimization. * Proficient in NLP techniques, Explainable AI, and ML frameworks.
Proficient in multiple optimization paradigms such as combinatorial optimization, gradient methods, or Bayesian optimization. * Proficient in NLP techniques, Explainable AI, and ML frameworks.
Proficient in multiple optimization paradigms such as combinatorial optimization, gradient methods, or Bayesian optimization. * Proficient in NLP techniques, Explainable AI, and ML frameworks.
Senior Staff AI Scientist
Atlanta, GA · On-site
Proficient in multiple optimization paradigms such as combinatorial optimization, gradient methods, or Bayesian optimization. * Proficient in NLP techniques, Explainable AI, and ML frameworks.
Senior Staff AI Scientist
Atlanta, GA · On-site
Proficient in multiple optimization paradigms such as combinatorial optimization, gradient methods, or Bayesian optimization. * Proficient in NLP techniques, Explainable AI, and ML frameworks.
Staff AI Scientist
Atlanta, GA · On-site
Proficient in multiple optimization paradigms such as combinatorial optimization, gradient methods, or Bayesian optimization. * Proficient in NLP techniques, Explainable AI, and ML frameworks.
Staff AI Scientist
Atlanta, GA · On-site
Proficient in multiple optimization paradigms such as combinatorial optimization, gradient methods, or Bayesian optimization. * Proficient in NLP techniques, Explainable AI, and ML frameworks.
Staff AI Scientist
Atlanta, GA · On-site
Proficient in multiple optimization paradigms such as combinatorial optimization, gradient methods, or Bayesian optimization. * Proficient in NLP techniques, Explainable AI, and ML frameworks.
Staff AI Scientist
Atlanta, GA · On-site
Proficient in multiple optimization paradigms such as combinatorial optimization, gradient methods, or Bayesian optimization. * Proficient in NLP techniques, Explainable AI, and ML frameworks.
Experience working on combinatorial optimization problems, advanced graph theory, advanced math and cloud capacity planning. * Experience in database design for analytical reporting, and familiarity ...
Experience working on combinatorial optimization problems, advanced graph theory, advanced math and cloud capacity planning. * Experience in database design for analytical reporting, and familiarity ...
Combinatorial Optimization information
See Georgia salary details
$35K - $47.3K
3% of jobs
$47.3K - $59.5K
0% of jobs
$59.5K - $71.8K
1% of jobs
$71.8K - $84K
4% of jobs
$84K - $96.3K
11% of jobs
$100.7K is the 25th percentile. Wages below this are outliers.
$96.3K - $108.5K
16% of jobs
The median wage is $119.2K / yr.
$108.5K - $120.7K
17% of jobs
$120.7K - $133K
16% of jobs
$138K is the 75th percentile. Wages above this are outliers.
$133K - $145.2K
17% of jobs
$145.2K - $157.5K
10% of jobs
$157.5K - $169.7K
5% of jobs
$35K
$120.3K
$169.7K
How much do combinatorial optimization jobs pay per year?
As of Jun 12, 2026, the average yearly pay for combinatorial optimization in Georgia is $120,291.00, according to ZipRecruiter salary data. Most workers in this role earn between $100,100.00 and $140,600.00 per year, depending on experience, location, and employer.
What are the key skills and qualifications needed to thrive as a Combinatorial Optimization Specialist, and why are they important?
To thrive as a Combinatorial Optimization Specialist, you need a solid background in mathematics, computer science, and operations research, often supported by an advanced degree in a related field. Familiarity with programming languages (such as Python, C++, or Java), optimization libraries, and mathematical modeling tools like CPLEX or Gurobi is typically required. Strong analytical thinking, problem-solving skills, and effective communication help you devise and explain complex solutions to stakeholders. These skills are crucial for developing efficient algorithms and models that address challenging optimization problems in various industries.
How does a Combinatorial Optimization specialist typically collaborate with other departments within an organization?
Combinatorial Optimization specialists frequently work cross-functionally, partnering with data scientists, software engineers, and business analysts to translate complex business problems into mathematical models. They help teams identify optimal solutions for scheduling, routing, resource allocation, and other operational challenges. Effective communication is crucial, as specialists must explain complex algorithms to non-technical stakeholders and integrate their solutions into broader business processes. Collaborative teamwork and iterative problem-solving are common in this role.
What is the difference between Combinatorial Optimization vs Data Analyst?
| Aspect | Combinatorial Optimization | Data Analyst |
|---|---|---|
| Required Credentials | Mathematics, Operations Research, Computer Science degrees | Statistics, Data Science, Business Analytics degrees |
| Work Environment | Research labs, consulting firms, tech companies | Corporate offices, finance, marketing departments |
| Industry Usage | Logistics, manufacturing, AI, supply chain | Finance, marketing, healthcare, retail |
While both roles involve analytical skills, Combinatorial Optimization focuses on solving complex mathematical problems to find optimal solutions, often in logistics and operations. Data Analysts interpret data to inform business decisions, working across various industries. Understanding these differences helps clarify career paths and employer expectations.
What is combinatorial optimization?
Combinatorial optimization is a field in mathematics and computer science focused on finding the best solution from a finite set of possible solutions. It involves problems where you need to arrange, select, or group discrete objects according to certain rules to achieve an optimal outcome. Examples include scheduling, routing, and assignment problems. Techniques such as linear programming, branch and bound, and heuristics are often used to solve these problems. Combinatorial optimization is widely applied in logistics, operations research, computer science, and engineering.
What are popular job titles related to Combinatorial Optimization jobs in Georgia? For Combinatorial Optimization jobs in Georgia, the most frequently searched job titles are:
What job categories do people searching Combinatorial Optimization jobs in Georgia look for? The top searched job categories for Combinatorial Optimization jobs in Georgia are:
What cities in Georgia are hiring for Combinatorial Optimization jobs? Cities in Georgia with the most Combinatorial Optimization job openings:
Intuit rating
8.3
Based on 82 frontline employees who took The Breakroom Quiz
73rd of 188 rated software companies
Job description
Intuit is looking for innovative and hands-on Senior Staff AI Scientist to join the Intuit AI team.
Come join our collaborative and creative group of AI scientists and machine learning engineers and build models that directly affect hundreds of thousands of our customers. In this role you will be building and deploying machine learning models using both analytical algorithms and deep learning approaches.
Responsibilities
Qualifications
Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position will be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at ). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender. The expected base pay range for this position is:
Come join our collaborative and creative group of AI scientists and machine learning engineers and build models that directly affect hundreds of thousands of our customers. In this role you will be building and deploying machine learning models using both analytical algorithms and deep learning approaches.
Responsibilities
- Practices leadership and communication skills to influence teams and to evangelize AI science across the organization
- Collaborates with stakeholders to define success criteria and align model metrics with business goals. Works side-by-side with product managers, software engineers, and designers in designing experiments and minimum viable products
- Leads technical work of a scrum team: initiating and designing model solutions, driving end-to-end architecture designs of the team's work, and holding the team accountable for high quality code, git, design, costs and implementation standards
- Performs hands-on data analysis and modeling with large data sets, including discovering data sources, getting data access, cleaning up data, and making them "model-ready". You need to be willing and able to do your own ETL and design/build featurization.
- Applies data mining, NLP, and machine learning (such as supervised/unsupervised, Causal-ML, Online Learning, Bayesian Learning, Reinforcement Learning, or Deep Learning) to real-world problems and datasets.
- Runs A/B tests to draw conclusions on the impact of your team's work and communicates results to peers and leaders
- Communicates with partners to ensure successful delivery and integration of DS solutions.
- Proactively researches, explores, and enables new ML technologies. Keeps up with the new developments in academia and industry and considers possible extensions to solve Intuit customer problems.
Qualifications
- 6+ years of industry experience with AI science
- BS, MS or PhD in Statistics, Mathematics, Computer Science, Economics, Operations Research, or equivalent
- 4+ years of hands-on expertise in ML paradigms such as Causal-ML, supervised/unsupervised, Online, Bayesian, Reinforcement or Deep Learning.
- Proficient in multiple optimization paradigms such as combinatorial optimization, gradient methods, or Bayesian optimization.
- Proficient in NLP techniques, Explainable AI, and ML frameworks.
- Expertise in modern advanced analytical tools and programming languages such as Python, Scala, Java and/or R.
- Efficient in SQL, Hive, SparkSQL, etc.
- Comfortable working in a Linux environment
- Experience with building end-to-end reusable pipelines from data acquisition to model output delivery
- Quick learner, adaptable, with the ability to work independently in a fast-paced environment
- Strong oral and written communication skills. Ability to conduct meetings and make professional presentations, and to explain complex concepts and technical material to non-technical users
Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position will be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at ). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender. The expected base pay range for this position is: