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Senior Machine Learning Finance Jobs in Austin, TX

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Senior Machine Learning Finance information

See Austin, TX salary details

$51.5K

$126.4K

$174.5K

How much do senior machine learning finance jobs pay per year?

As of Sep 5, 2026, the average yearly pay for senior machine learning finance in Austin, TX is $126,444.00, according to ZipRecruiter salary data. Most workers in this role earn between $108,000.00 and $147,200.00 per year, depending on experience, location, and employer.

What is the difference between Senior Machine Learning Finance vs Quantitative Analyst?

AspectSenior Machine Learning FinanceQuantitative Analyst
Required CredentialsAdvanced degrees in CS, Data Science, or Finance; experience with ML frameworksDegrees in Finance, Economics, or Mathematics; strong statistical background
Work EnvironmentTech-driven finance teams, focus on ML model developmentTrading floors, investment firms, focus on quantitative modeling
Industry UsageFinancial institutions leveraging AI/ML for trading, risk, and asset managementAsset management, hedge funds, investment banks

While both roles involve quantitative skills, Senior Machine Learning Finance focuses on developing and deploying machine learning models within finance, whereas Quantitative Analysts primarily build statistical models for trading and risk assessment. The former emphasizes AI/ML expertise, while the latter centers on traditional quantitative methods.

What are the most commonly searched types of Machine Learning Finance jobs in Austin, TX?

The most popular types of Machine Learning Finance jobs in Austin, TX are:

Senior Machine Learning Finance jobs near you

Senior Machine Learning Engineer, Causal & Decision Systems

CSC Generation Enterprise

Austin, TX • On-site

$140 - $180/hr

Other

Posted 18 days ago

Job description

CSC Generation is building closed-loop decision systems that use machine learning to operate consumer businesses more intelligently.

We are starting with pricing and expanding into areas such as inventory, purchasing, promotions, marketing, and assortment.

The Role

You will help build systems that:

estimate causal response + quantify uncertainty → choose actions → generate useful information → observe outcomes → update policies → evaluate challengers → deploy within guardrails

We want to answer questions such as:

  • What happens because we change a price, rather than simply what happens next?
  • How should uncertainty affect a decision?
  • When should the system exploit what it knows versus experiment to learn?
  • Can we estimate the value of a challenger policy before fully deploying it?
  • How do we optimize economic outcomes while respecting inventory, margin, vendor, customer, and operational constraints?
What You’ll Work On

Depending on your background, you may work across:

  • causal and heterogeneous treatment-effect modeling;
  • uncertainty estimation and calibration;
  • contextual bandits, active learning, or sequential decision-making;
  • policy learning and constrained optimization;
  • counterfactual and off-policy evaluation;
  • experimentation and champion/challenger systems;
  • production ML infrastructure, monitoring, and automated deployment.

We care about selecting the right method, not using a particular framework.

What Success Looks Like

Success is not a better offline metric.

The systems you build should produce measurable economic lift in controlled experiments, generalize across businesses, learn from their own interventions, and safely automate an increasing share of real commercial decisions.

Over time, the goal is simple:

the system should become better at operating the business because it has operated the business.

What We’re Looking For

We care more about exceptional technical ability and judgment than matching a checklist.

Strong candidates will have experience in several of:

  • machine learning and statistical modeling;
  • causal inference and experimentation;
  • recommendation, advertising, pricing, marketplace, credit, or other decision systems;
  • bandits, reinforcement learning, optimization, or active learning;
  • uncertainty estimation;
  • counterfactual evaluation;
  • production ML systems;
  • Python, SQL, and large behavioral datasets.
Why This Role Is Different

Most ML systems learn from a dataset.

Here, the decisions made by the model influence the data the model sees next.

That creates a continuous loop:

Decision → intervention → outcome → learning → better decision

The long-term opportunity is to build that capability once and apply it across a portfolio of businesses and increasingly broad commercial decisions.

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