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Machine Learning Economics Jobs (NOW HIRING)

The Team Our Core ML organization is looking for an exceptional, hands-on Machine Learning Manager ... Keen statistical, economic and business intuition, and comfort with reasoning under uncertainty and ...

Senior Machine Learning Engineer

Atlanta, GA · On-site

$117K - $155K/yr

... and economics. At Inovalon, we believe that when our customers are successful in their missions ... The Senior Full-Stack Machine Learning Engineer sits within the Insights Business Unit, which ...

Senior Machine Learning Engineer

Nashville, TN · On-site

$118K - $156K/yr

... and economics. At Inovalon, we believe that when our customers are successful in their missions ... The Senior Full-Stack Machine Learning Engineer sits within the Insights Business Unit, which ...

The Team Our Core ML organization is looking for an exceptional, hands-on Machine Learning Manager ... Keen statistical, economic and business intuition, and comfort with reasoning under uncertainty and ...

Senior Machine Learning Engineer

Houston, TX · On-site

$116K - $154K/yr

They are seeking an experienced Machine Learning Engineer to join their data science and machine ... and econometric approaches (e.g. ARIMA, cointegration, regime-switching models) • Data ...

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Machine Learning Economics information

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How much do machine learning economics jobs pay per hour?

As of Jun 23, 2026, the average hourly pay for machine learning economics in the United States is $65.38, according to ZipRecruiter salary data. Most workers in this role earn between $59.62 and $71.15 per hour, depending on experience, location, and employer.

How much do economists in AI make?

Economists working in AI typically earn between $80,000 and $150,000 annually, depending on experience, education, and location. Those with specialized skills in machine learning, data analysis, and programming tools like Python or R tend to have higher salaries, especially in tech hubs or research institutions.

Is ML a high paying job?

Machine Learning roles are generally well-paid due to the specialized skills required, such as programming, statistics, and data analysis. Salaries vary based on experience, location, and industry, but many machine learning positions offer competitive compensation compared to other tech roles.

Will AI take econ jobs?

Machine Learning Economics involves applying AI and data analysis to economic problems, and while AI can automate certain tasks, it is unlikely to fully replace economists. Instead, AI tools are used to enhance economic research, modeling, and decision-making, requiring specialized skills in both economics and machine learning. Economists who adapt to these technologies can find new opportunities in data-driven analysis and policy development.

Is machine learning useful for economists?

Machine learning is increasingly valuable for economists, enabling them to analyze large datasets, identify patterns, and improve predictive models. Skills in programming languages like Python or R and understanding of statistical methods are essential for applying machine learning effectively in economic research and policy analysis.

What is the difference between Machine Learning Economics vs Data Scientist?

AspectMachine Learning EconomicsData Scientist
Required CredentialsDegree in Economics, Data Science, or related fields; knowledge of ML and economicsDegree in Computer Science, Statistics, or related fields; strong programming skills
Work EnvironmentResearch-focused, often in finance, tech, or consulting firmsData analysis, modeling, and visualization across various industries
Industry UsageFinance, tech, policy analysis, consultingTech, healthcare, finance, retail, and more

Machine Learning Economics combines economic theory with machine learning techniques to analyze market behaviors and policy impacts, often requiring knowledge of economics and ML. Data Scientists focus on extracting insights from data using statistical and ML methods across diverse industries. While both roles involve data analysis and programming, Machine Learning Economics emphasizes economic modeling, whereas Data Scientists have a broader scope in data-driven decision-making.

Infographic showing various Machine Learning Economics job openings in the United States as of June 2026, with employment types broken down into 14% Full Time, 85% Part Time, and 1% Nights. Highlights an 73% Physical, 7% Hybrid, and 20% Remote job distribution, with an average salary of $135,999 per year, or $65.4 per hour.
Senior Manager, Machine Learning

Senior Manager, Machine Learning

Upstart

OR • Remote

Other

Posted 18 days ago


Job description

The Team 

Our Core ML organization is looking for an exceptional, hands-on Machine Learning Manager to join our leadership group. Because our ML teams share common codebases and modeling pipelines, we are searching for generalist ML leaders who can be deployed to the areas of our business where they will have the most impact.

Rather than hiring for one specific silo, we match candidates to the right team based on their unique background, technical strengths, and interests. Depending on your expertise, you could step in to lead one of several high-priority teams, such as:

  • Cash Line: Leading the 01 ML innovation for our brand new subscription-based line of credit, building core underwriting and customer behavior models (churn, draw, default) in a domain with limited data and long feedback loops.
  • Auto Retail Lending (ARL): Tackling unique, deep-modeling challenges such as competing risk, collateral, and recovery modeling for dealership-based auto lending.
  • Underwriting: Managing MLE-heavy engineering and research efforts to optimize our core unsecured personal loan models.

This is a highly technical player-coach role. You will not be managing a massive organization; instead, you will lead a small, nimble team of individual contributors (Research Scientists, Data Scientists, or Machine Learning Engineers). This role is designed for a builder who wants to retain meaningful strategic scope, maintain a roughly 50/50 split between technical execution and management, and act as the definitive ML owner for their product space.

How you'll make an impact:

  • Act as a Player-Coach: Dive deep into the data and code. You will spend a significant portion of your time making direct technical contributions, reviewing code/PRs, and understanding the mathematical nuances of your team's models.
  • Lead Strategic Initiatives: Take ownership of a specific product area (like Cash Line or Auto) and serve as the de facto ML leader in cross-functional strategy meetings.
  • Drive 01 and Scaling ML Efforts: Depending on your team placement, you may build out entirely new capabilities from scratch or optimize highly mature models dealing with massive scale and shifting macro-economic regimes.
  • Translate Models to Business Impact: Design and refine decision engines that translate model predictions into accurate, transparent, and customer-friendly lending outcomes.

What we're looking for:

  • Minimum Qualifications
    • Experience
      • 6+ years of experience developing and deploying machine learning models in production with direct business impact.
      • Proven track record of leading high-impact ML initiatives from research through productionization.
      • Advanced degree in a quantitative field (e.g., computer science, statistics, economics, operations research, etc).
    • Technical abilities and attitude
      • Strong technical judgment and ability to dive deep into model design, data analysis, and evaluation. 
      • Keen statistical, economic and business intuition, and comfort with reasoning under uncertainty and data limitations.
      • Knowledge of production ML, ability to adapt to new tech stacks and get in the weeds of work output from the team. 
      • Excited and able to make direct technical contributions when needed.
    • Leadership
      • Exceptional leadership skills with experience developing teams of highly technical ML scientists.
      • Attract, mentor, and grow a top-tier team of ML scientists passionate about expanding access to credit.
      • Proven ability to influence and collaborate cross-functionally with Product, Engineering, Capital Markets and other teams.
      • Strong project management skills with experience scaling processes and operational workflows.
  • Preferred Qualifications
    • PhD in Computer Science, Statistics, Economics or a related field.
    • Proven success building and scaling new ML products from inception.
    • Familiarity with lending, lines of credit, or other consumer finance products. 
    • Hands-on familiarity with end-to-end ML infrastructure, including experimentation pipelines, feature stores, and model monitoring. Ability to uplevel team's engineering practices and drive cross-functional engineering design.

Position Location - This role is available in the following locations: US Remote

Time Zone Requirements - This team operates on the East/West Coast time zones.

Travel Requirements - This team has regular on-site collaboration sessions. These occur 3-4 days per quarter at one of our offices. If you need to travel to make these meetups, Upstart will cover all travel related expenses.

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