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Contract Data Scientist Risk Jobs (NOW HIRING)

Jobtailor

Data Scientist, Fraud Risk

California, MO · On-site

Data Science * Risk Analytics * Model Validation * Feature Engineering * Machine Learning * Fraud Detection * KYC Controls * AML Compliance * Data Transformation * Decision System Evaluation Soft ...

New

Flexcar LLC

Risk Data Scientist

Boston, MA · On-site

Risk Data Scientist Location: Downtown B oston - Preferred Onsite-CA/GA/TX -Optional Onsite hub locations Employment Type: Exempt Full Time Compensation: $112,000 - $155,000 + Full Benefit Package ...

Flexcar

Risk Data Scientist

Boston, MA · On-site

Risk Data Scientist Location: Downtown B oston - Preferred Onsite-CA/GA/TX -Optional Onsite hub locations Employment Type: Exempt Full Time Compensation: $112,000 - $155,000 + Full Benefit Package ...

Flexcar

Risk Data Scientist

Boston, MA · On-site

Risk Data Scientist Location: Downtown Boston, MA Employment Type: Exempt Full Time - 5 days in office - 50 hours Compensation: $112,000 - $155,000 + Full Benefit Package Day One * This position is ...

Imprint

Data Scientist, Fraud Risk

New York, NY · On-site +1

$170K - $200K/yr

The Team The Risk team at Imprint builds the models, policies, and analytical systems that protect our credit card programs while delivering a fast and seamless member experience. As a Data Scientist ...

LexisNexis Risk Solutions

Data Scientist

Alpharetta, GA · On-site

LexisNexis Risk Solutions is the essential partner in the assessment of risk. Within Insurance, we ... We are looking for a Sr. Data Scientist I with strong expertise in statistics/modeling and machine ...

TAD PGS, Inc.

Data Scientist

Washington, DC · On-site

$52.88 - $57.69/hr

We have an outstanding Contract position for a Data Scientist to join a leading Company located in ... predictive risk models backed by statistical analysis and supports data-driven solutions.

Neara

Principal Research Scientist - Risk

New York, NY · On-site

$180K - $220K/yr

Neara provides network-scale structural, loading, and contextual data across complete overhead electrical systems. As the Principal Research Scientist - Risk, you will use this information to build ...

Neara

Neara provides network-scale structural, loading, and contextual data across complete overhead electrical systems. As the Principal Research Scientist - Risk, you will use this information to build ...

Neara

Principal Research Scientist - Risk

Manhattan, NY · On-site

Neara provides network-scale structural, loading, and contextual data across complete overhead electrical systems. As the Principal Research Scientist - Risk, you will use this information to build ...

Analygence

Data Scientist

Arlington, VA · On-site

Tharros is seeking a Data Scientist to serve as the program's senior analytical authority, leading ... Design, build, and sustain analytical models that support COA analysis, risk prioritization ...

BigBear.ai

Data Scientist

Mclean, VA · On-site

... risk signals into a transparent, defensible score. This role also prepares the scoring approach and ... logic (data inputs, contracts, output formats, and performance expectations). * Establish ...

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How much do contract data scientist risk jobs pay per year?

As of Sep 11, 2026, the average yearly pay for contract data scientist risk in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.

What is a contract data scientist risk?

Contract Data Scientist Risk jobs involve analyzing large datasets to identify, assess, and model risks within an organization on a contract or temporary basis. These professionals use statistical techniques, machine learning, and data visualization to help companies make informed decisions about risk management. Their work often focuses on financial, operational, or compliance risks, and they typically collaborate with risk managers and other stakeholders. Contract roles offer flexibility and are often project-based, allowing companies to access specialized expertise when needed.

How does a contract data scientist risk typically collaborate with cross-functional teams within an organization?

As a Contract Data Scientist focused on risk, you’ll frequently work alongside risk analysts, compliance officers, IT professionals, and business managers to identify, assess, and mitigate potential threats to the organization. You’ll translate complex data models into actionable insights, present findings to non-technical stakeholders, and often participate in strategic meetings to align your analyses with business goals. Effective communication and teamwork are essential, as your models and recommendations directly influence decision-making across departments. This collaborative environment allows you to quickly gain exposure to different areas of the business and expand your professional network.

What are the key skills and qualifications needed to thrive as a contract data scientist risk, and why are they important?

To thrive as a Contract Data Scientist in Risk, you need strong analytical skills, proficiency in statistical modeling, and a solid background in mathematics or computer science, often supported by an advanced degree. Familiarity with programming languages like Python or R, expertise in machine learning libraries, and experience with risk modeling platforms or data visualization tools are typically required. Outstanding problem-solving abilities, attention to detail, and effective communication make someone stand out in this position. These skills and qualities are crucial for accurately identifying, assessing, and mitigating risks, enabling data-driven decision-making in dynamic business environments.
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Contract Data Scientist Risk jobs near you
Infographic showing various Contract Data Scientist Risk job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 84% Full Time, 11% Part Time, and 3% Contract. Highlights an 85% Physical, 3% Hybrid, and 12% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.

Data Scientist, Fraud Risk

California, MO • On-site

Jobtailor
See more jobs at Jobtailorexternal

Other

Posted 3 days ago

New

Job description

Own and improve Imprint's onboarding fraud decisioning across the full application journey
Develop identity verification, KYC controls, application fraud models, policy rules, decline and verification waterfalls, and manual-review strategies
Build, validate, deploy, and monitor models detecting identity theft, synthetic identity, first-party fraud, and coordinated application abuse
Evaluate third-party fraud and identity vendors using lift, overlap, coverage, stability, latency, and cost metrics
Design and analyze A/B tests, shadow tests, holdouts, and champion/challenger strategies
Balance fraud losses and capture against approval rate, false positives, verification friction, and manual-review volume
Investigate emerging fraud patterns and decision misses using application, post-booking, and Fraud Operations data
Build monitoring and AI-powered workflows to detect model drift, population shifts, vendor degradation, data-quality issues, and new attack patterns
Partner with Fraud Operations, Product, Engineering, Compliance, and Credit Strategy to productionize changes
Validate impact and communicate recommendations to senior leadership and external partners
Help protect credit card programs while delivering fast, seamless member experiences

Requirements
  • 5 to 8+ years of experience in data science, risk analytics, or a related quantitative field
  • Strong Python and SQL skills
  • Ability to build models, transform raw data, and create custom datasets from complex financial data
  • Experience building and evaluating predictive models for fraud, identity, KYC, AML, credit risk, trust and safety, or another adversarial classification problem
  • Strong understanding of supervised machine learning, model validation, backtesting, calibration, feature engineering, and production model monitoring
  • Deep understanding of statistical inference and experiment design, including A/B tests, holdouts, champion/challenger tests, causal measurement, and tradeoff analysis
  • Ability to evaluate decision systems using fraud capture, loss rate, false-positive rate, approval impact, verification friction, operational workload, and economic value
  • Ability to trace decisions through raw inputs, vendor responses, model scores, policy rules, and downstream outcomes
  • Comfort owning projects end-to-end from problem definition through production implementation, monitoring, and business impact measurement
  • Ability to communicate complex analytical findings and decision tradeoffs to technical and non-technical audiences
  • Comfort using AI tools for analysis, investigation, feature development, documentation, and monitoring
  • Experience with Snowflake, AWS infrastructure, dashboarding, and production monitoring tools
Core Competencies

Demonstrates expertise in building and validating predictive models for fraud detection, identity verification, and KYC controls, while effectively communicating analytical findings to diverse audiences. Proficient in leveraging AI tools and statistical methods to enhance decision-making processes and operational efficiency.

Highest-signal resume keywords
  • Python Programming
  • SQL Proficiency
  • Predictive Model Development
  • Statistical Inference
  • A/B Testing Design
Hard Skills
  • Data Science
  • Risk Analytics
  • Model Validation
  • Feature Engineering
  • Machine Learning
  • Fraud Detection
  • KYC Controls
  • AML Compliance
  • Data Transformation
  • Decision System Evaluation
Soft Skills
  • Communication
  • Project Ownership
  • Analytical Thinking
  • Collaboration
  • Problem Solving
Industry Keywords
  • Fraud Decisioning
  • Identity Verification
  • Application Fraud Models
  • Credit Risk
  • Operational Workload
Tools & Technologies
  • Snowflake
  • AWS Infrastructure
  • Dashboarding Tools
  • Production Monitoring Tools
  • AI Tools
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