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Risk Data Science Research Jobs (NOW HIRING)

BillGO, Inc.

AI Data Scientist

Fort Collins, CO · On-site

$102K - $146K/yr

You'll work on developing intelligent systems that power risk modeling, fraud prevention, customer ... Research & Innovation: Stay on top of advancements in generative AI, LLMs, and financial AI ...

Hirevouch

Data Scientist

Develop and implement advanced predictive models to forecast key risk factors relevant to insurance ... Stay at the forefront of data science research, exploring new methodologies and technologies to ...

FloatMe

Senior/Staff Data Scientist

Austin, TX · On-site

... and risk to align Data Science work with broader strategy Who You Are * 4+ years applying AI ... Research). A PhD degree is welcomed. * Advanced proficiency with SQL, Python and experience ...

FloatMe

Senior/Staff Data Scientist

San Antonio, TX · On-site

... and risk to align Data Science work with broader strategy Who You Are * 4+ years applying AI ... Research). A PhD degree is welcomed. * Advanced proficiency with SQL, Python and experience ...

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How much do risk data science research jobs pay per year?

As of Sep 13, 2026, the average yearly pay for risk data science research in the United States is $142,460.00, according to ZipRecruiter salary data. Most workers in this role earn between $118,500.00 and $166,500.00 per year, depending on experience, location, and employer.

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Infographic showing various Risk Data Science Research job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 83% Full Time, 12% Part Time, and 4% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $142,460 per year, or $68.5 per hour.

AI Data Scientist

Fort Collins, CO • On-site

BillGO, Inc.
Finance and Insurance • 201 - 500 employees
Learn more about BillGO, Inc.external

$102K - $146K/yr

Full-time

Re-posted 12 hours ago

Job description

About the Role
We're looking for a Data Scientist with deep AI and machine learning expertise to help shape the future of data-driven innovation in fintech. You'll work on developing intelligent systems that power risk modeling, fraud prevention, customer insights and targeting, and payment optimization. Your models will have a direct impact on financial decisions, operational efficiency, and customer trust across our products.
Key Responsibilities
  • AI-Driven Insights: Develop and deploy advanced machine learning models to optimize customer targeting, payment monitoring and growth and operational efficiency opportunities.
  • Predictive Modeling: Build forecasting models to improve transaction accuracy, detect anomalies, and assess financial risk.
  • Data Engineering & Feature Design: Clean, transform, and model large, high-velocity financial datasets with attention to data integrity and compliance.
  • AI Product Integration: Collaborate with Product to integrate AI solutions into production systems for real-time financial decisioning.
  • Experimentation: Lead A/B tests and model performance evaluations to validate model effectiveness and regulatory compliance.
  • Communication: Translate technical findings into actionable insights for business leaders and compliance teams.
  • Research & Innovation: Stay on top of advancements in generative AI, LLMs, and financial AI applications to guide innovation strategy.

Required Qualifications
• Bachelor's or Master's degree in Data Science, Computer Science, Statistics, or related field.
• 3+ years of experience in a data science or AI-focused role within fintech, banking, or payments.
• Expertise in Python, machine learning frameworks (scikit-learn, TensorFlow, PyTorch), and data pipelines.
• Strong background in supervised/unsupervised learning, anomaly detection, NLP, and generative AI.
• Familiarity with financial data structures, regulatory standards (e.g., PCI-DSS, GDPR), and model governance.
• Experience with cloud platforms such as Snowflake for ML deployment.
Preferred Qualifications
• Experience in fraud analytics, risk scoring, or payment decision models.
• Understanding of MLOps and continuous model monitoring in regulated environments.
• Familiarity with financial transaction data, open banking APIs, or real-time payments systems.
• Experience developing LLM-powered assistants or AI copilots for financial operations or support.
• Strong data storytelling and visualization skills (Tableau preferred).

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