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Open nowPosted 24 hours ago

AI Data Scientist

Ultimus43 open roles

Where
Omaha, NE, USA
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Your applicationOpen nowAI Data ScientistUltimus · Omaha, NE, USA
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The clock on this job

Early applications get read.

13.6% of postings close within 7 days. Measured by our own scanner across the market. Ultimus postings stay open a median of 21 days.

Share of postings closed within
  1. 2.3%1 day
  2. 4.7%3 days
  3. 13.6%7 days
  4. 21.9%14 days
  5. 36.9%30 days
  6. 59.0%60 days
This job: posted 24 hours ago

Ultimus median: 21 days open

The posting

Position Summary The AI Data Scientist will own the modeling and statistical lifecycle of enterprise AI initiatives. The role will partner with business operations to structure financial data, build predictive models, and ensure AI investments deliver high-accuracy insights that reduce risk and improve decision-making. The role requires a rigorous quantitative expert who can balance complex statistical analysis with strict financial regulatory standards and drive alignment across business and technology teams. Key Responsibilities

Develop and train advanced predictive models for anomaly detection, workflow forecasting, and risk mitigation Perform deep statistical analysis and data wrangling within large-scale enterprise environments Validate model accuracy rigorously, test for bias, and ensure all outputs meet strict financial compliance standards Partner with business leaders to understand the nuance of financial data and transform raw inputs into training datasets Establish KPIs tied to model accuracy, prediction precision, and false-positive reduction

Qualifications

5+ years of experience building and validating predictive models in a corporate or financial environment Demonstrated ability to apply advanced statistics and machine learning to deliver measurable business outcomes Deep expertise in statistical analysis, Python, R, and data science frameworks Ability to work effectively with operational teams to understand data context without needing constant translation Strong understanding of financial datasets and the regulatory constraints surrounding automated decision-making Ability to present complex quantitative findings to non-technical stakeholders Operator mindset with a focus on execution and analytical outcomes

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