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Home Based Convex Optimization Jobs (NOW HIRING)

Federato Technologies

Data Scientist

San Francisco, CA · Remote

$160K - $200K/yr

Company Description Federato Technologies is a Series A startup based in San Fransisco, CA looking ... Graduate work in an optimization related field (e.g RL, Convex Optimization, Bayesian Optimization ...

Federato Technologies

Data Scientist

San Francisco, CA · On-site +1

$160K - $200K/yr

Company Description Federato Technologies is a Series A startup based in San Fransisco, CA looking ... Graduate work in an optimization related field (e.g RL, Convex Optimization, Bayesian Optimization ...

Vise

Quantitative Engineer

New York, NY

$190K - $270K/yr

We work closely with convex optimization techniques and numerical optimizations of various problems ... Your actual compensation will be determined based on your skills, qualifications, and experience.

Vise

Quantitative Engineer

New York, NY · On-site

$190K - $270K/yr

We work closely with convex optimization techniques and numerical optimizations of various problems ... Your actual compensation will be determined based on your skills, qualifications, and experience.

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All JobsHome Based Convex Optimization Jobs

Home Based Convex Optimization information

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$16K

$55.8K

$102K

How much do home based convex optimization jobs pay per year?

As of Jun 19, 2026, the average yearly pay for home based convex optimization in the United States is $55,794.00, according to ZipRecruiter salary data. Most workers in this role earn between $36,000.00 and $72,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Home-Based Convex Optimization Specialist, and why are they important?

To excel as a Home-Based Convex Optimization Specialist, you need a strong background in mathematics, particularly linear algebra and calculus, along with experience in optimization theory and a relevant degree such as mathematics, engineering, or computer science. Proficiency with technical tools like MATLAB, Python (with libraries such as CVXPY), and optimization solvers is typically required. Critical thinking, problem-solving, and effective remote communication are essential soft skills for success in this independent, analytical role. These skills are crucial for accurately modeling, solving complex optimization problems, and collaborating efficiently with remote teams or clients.

What are some common challenges faced by professionals working in home-based convex optimization roles, and how can they be addressed?

One common challenge in home-based convex optimization roles is maintaining effective communication with team members, especially when collaborating on complex mathematical models or sharing large datasets. To address this, professionals often use collaborative tools such as cloud-based platforms and version control systems to facilitate seamless workflow and project tracking. Additionally, the solitary nature of remote work can make problem-solving more difficult, so regular virtual meetings and knowledge-sharing sessions are essential for fostering a supportive team environment. Staying updated with the latest research and optimization software also helps in overcoming technical obstacles and enhancing productivity.

What is the difference between Home Based Convex Optimization vs Data Scientist?

AspectHome Based Convex OptimizationData Scientist
Required CredentialsMathematics, Optimization, Computer Science degreesStatistics, Mathematics, Computer Science degrees
Work EnvironmentRemote, independent work on optimization problemsRemote or office, analyzing data and building models
Industry UsageFinance, tech, research institutionsTech, finance, healthcare, marketing

Home Based Convex Optimization specialists focus on solving mathematical optimization problems remotely, often within research or technical roles. Data Scientists analyze data to extract insights and build predictive models. While both roles require strong analytical skills and related credentials, their core tasks differ: one emphasizes mathematical problem-solving, the other data analysis. They are often searched together due to overlapping skills and remote work options.

What is a Home Based Convex Optimization job?

A Home Based Convex Optimization job involves working remotely to solve mathematical problems where the objective function is convex, meaning any local minimum is a global minimum. Professionals in this role typically use advanced mathematical and computational techniques to optimize processes, systems, or models across various industries, such as finance, engineering, or machine learning. Tasks may include developing algorithms, implementing optimization models, and analyzing data sets to find optimal solutions. These jobs often require a strong background in mathematics, computer science, and experience with optimization software or programming languages.
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Home Based Convex Optimization jobs near you
Data Scientist

Data Scientist

Federato Technologies

San Francisco, CA • Remote

$160K - $200K/yr

Full-time

Posted 16 days ago

Job description

Company Description

Federato Technologies is a Series A startup based in San Fransisco, CA looking to hire a data scientist. Federato is a venture backed company funded by some of the most prominent VC's in the Bay Area. We build software to help large insurance carriers improve their portfolio optimization. Our goal is to close down the $150B insurance gap by making insurance affordable again. The initial algorithms used in our product spun out off the founders published research in Reinforcement Learning, Statistics, and Optimization.

The role will report directly to the CTO. Next to the CTO, you will be first Data Scientist on the team building out models and algorithms pivotal to our value proposition. 

This is a remote position, but we do have an office in San Fransisco.

Job Description

You will be the first data scientist on the team working through and building models from scratch that will be pivotal for our business. You will have a huge impact on our success and will be part of a fast growing team!

Qualifications

  • Demonstrated interest in engineering for impact

  • 2+ years of hands-on industry experience with machine learning and optimization models

  • 2+ years of experience with model deployment, monitoring and version control

  • Graduate work in an optimization related field (e.g RL, Convex Optimization, Bayesian Optimization), either PhD or Advanced MS degree.

  • Comfortable with Python, Flask/Django, Pandas and Numpy

  • Demonstrated ability to pick up new technologies quickly and learn on the spot

Additional Information

Pay Range: $160k - $200k

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