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Mathematical Optimization Remote Jobs in California

Adidev Technologies Inc

Data Science

San Francisco, CA · Remote

Optimization (Linear programming, Stochastic Gradient Descent, Genetic Algorithm etc.) * Experience ... Remote Support * Guaranteed Regular Salary Reviews * Job Type: W2 or Contract 1099 (full-time - 40 ...

NxT Level

Senior Tools Engineer

Los Angeles, CA · On-site +1

$112K - $154K/yr

Our client is looking for a Tools Programmer to join their fully remote team to build next ... Solid mathematical skills, particularly in linear algebra and geometry. * Experience with terrain ...

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Mathematical Optimization Remote information

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 the most commonly searched types of Mathematical Optimization jobs in California?

The most popular types of Mathematical Optimization jobs in California are:

What job categories do people searching Mathematical Optimization Remote jobs in California look for?

The top searched job categories for Mathematical Optimization Remote jobs in California are:

What cities in California are hiring for Mathematical Optimization Remote jobs?

Cities in California with the most Mathematical Optimization Remote job openings:

Mathematical Optimization Remote jobs near you

Sr. Machine Learning Engineer, tvScientific

Pinterest

San Francisco, CA • On-site, Remote

$144K - $190K/yr

Full-time

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

Key responsibilities

  • Write production Python code for real-time bidding, model training, and campaign optimization.

  • Train, deploy, and monitor ML models that determine ad selection, timing, and pricing at scale.

  • Build and improve incrementality measurement systems to assess the causal impact of CTV advertising.

Job description

About tvScientific

tvScientific is the first and only CTV advertising platform purpose-built for performance marketers. We leverage massive data and cutting-edge science to automate and optimize TV advertising to drive business outcomes. Our solution combines media buying, optimization, measurement, and attribution in one, efficient platform. Our platform is built by industry leaders with a long history in programmatic advertising, digital media, and ad verification who have now purpose-built a CTV performance platform advertisers can trust to grow their business.

As a Sr. Machine Learning Engineer at tvScientific, you'll build the ML and AI systems behind our Connected TV ad-buying platform: real-time bidding, campaign optimization, and incrementality measurement at scale. We're an adtech company solving a hard problem: making CTV advertising actually measurable. Our platform helps advertisers buy ads across the CTV ecosystem: Hulu, Pluto TV, Disney+, HBO Max, and hundreds of FAST channels: and prove that those ads drove real business outcomes.


What you'll do:

  • Write production Python that powers real-time bidding, model training, and campaign optimization
  • Train, deploy, and monitor ML models that decide which ads to show, when, and at what price: millions of bid decisions per second
  • Build and improve our incrementality measurement systems: helping advertisers understand the true causal lift of their CTV spend
  • Design and implement new ML products across the ad-buying lifecycle: audience targeting, bid optimization, pacing, and attribution
  • Use LLMs and generative AI to build internal tools that accelerate how we develop, test, and ship ML systems
  • Serve as a technical lead and mentor on a distributed engineering team


What we're looking for:

  • Strong production Python skills: you write code that runs in prod, not just notebooks
  • Solid statistics and ML fundamentals: you can reason about experiment design, model evaluation, and when simpler approaches beat complex ones
  • Familiarity with modern AI tools and good judgment about where they add value
  • Adtech or CTV experience: familiarity with RTB, programmatic advertising, supply-path optimization
  • Clear written communication: we're a distributed team and writing is how decisions get made
  • Comfort with ambiguity: you'll own problems end-to-end in a fast-moving environment, from scoping to shipping
  • Bachelor's degree in Computer Science, Mathematics, Engineering, related field, or equivalent experience
  • 4+ years of industry experience
  • Nice-to-Haves:
    • Experience using Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring
    • Familiarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL/data exploration, and engineering workflow acceleration
    • Causal inference: uplift modeling, synthetic controls, difference-in-differences, or incrementality testing
    • Big data experience with Scala and Spark
    • Systems programming experience in Zig or similar (C, C++, Rust)
    • Reinforcement learning or bandit algorithms in production
    • Experience building agentic AI systems or LLM-powered workflows
    • MLOps experience: model deployment, monitoring, and pipeline orchestration on AWS

In-Office Requirement Statement:

  • We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.


Relocation Statement:

  • This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.

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