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Data Scientist Fraud Detection Jobs in Delaware (NOW HIRING)

Citi

Specialized Analytics Senior Analyst

New Castle, DE

$83K - $105K/yr

Strong intellectual curiosity and eagerness to stay updated with the latest developments in data science, machine learning, and fraud detection techniques. 609912 ----- Job Family Group: Decision ...

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WSFS Bank

Fraud Product Manager

Wilmington, DE · On-site

$77 - $127/hr

... vendors, integrations, and data flows.* Evaluate how effectively current fraud tooling is ... Identify gaps and vulnerabilities in current fraud prevention and detection capabilities across ...

WSFS Bank

Fraud Product Manager

Wilmington, DE · On-site

$77K - $127K/yr

... data flows. * Evaluate how effectively current fraud tooling is configured and used, including ... Identify gaps and vulnerabilities in current fraud prevention and detection capabilities across ...

Wsfsbank

Fraud Product Manager

Wilmington, DE · On-site

$77K - $127K/yr

... data flows. * Evaluate how effectively current fraud tooling is configured and used, including ... Identify gaps and vulnerabilities in current fraud prevention and detection capabilities across ...

WSFS Bank

Fraud Product Manager

Wilmington, DE · On-site

$77K - $127K/yr

... data flows. * Evaluate how effectively current fraud tooling is configured and used, including ... Identify gaps and vulnerabilities in current fraud prevention and detection capabilities across ...

Barclays

Fraud Strategy Lead AVP

Wilmington, DE · On-site

$160 - $210/hr

... detection activities. * Management and development of KPIs to measure the effectiveness of customer care fraud prevention operations, utilising data and technology to support the identification of ...

New

Barclays

Fraud Strategy Lead AVP

Wilmington, DE · On-site

... detection activities. * Management and development of KPIs to measure the effectiveness of customer care fraud prevention operations, utilising data and technology to support the identification of ...

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Data Scientist Fraud Detection information

What does a data scientist in fraud detection do?

A Data Scientist in Fraud Detection analyzes large datasets to identify patterns and anomalies that could indicate fraudulent activities. They use machine learning algorithms, statistical models, and data mining techniques to detect and prevent fraud in areas like banking, insurance, and e-commerce. Their work helps organizations proactively combat fraud by developing predictive models and automated systems that flag suspicious transactions. Additionally, they often collaborate with other departments to refine detection strategies and ensure compliance with regulations.

What are the key skills and qualifications needed to thrive as a data scientist in fraud detection?

To thrive as a Data Scientist in Fraud Detection, you need a strong background in statistics, machine learning, and data analysis, typically supported by a degree in computer science, mathematics, or a related field. Familiarity with programming languages like Python or R, experience with big data tools (e.g., Hadoop, Spark), and knowledge of fraud detection platforms are essential. Strong problem-solving abilities, attention to detail, and effective communication skills set candidates apart in this field. These skills and qualities are crucial for identifying fraudulent activities quickly and accurately, minimizing financial losses, and supporting organizational security.

How does a data scientist in fraud detection typically collaborate with other teams to develop effective solutions?

As a Data Scientist in Fraud Detection, you will regularly collaborate with cross-functional teams such as fraud analysts, software engineers, and product managers. Working closely with fraud analysts helps you understand emerging fraud patterns, while partnering with engineers ensures your models are effectively integrated into real-time systems. You may also coordinate with compliance and legal teams to ensure solutions meet regulatory requirements. This collaborative approach not only improves the accuracy and impact of fraud detection models but also fosters a dynamic, supportive work environment.

What is the difference between Data Scientist Fraud Detection vs Data Analyst Fraud Detection?

AspectData Scientist Fraud DetectionData Analyst Fraud Detection
Required CredentialsBachelor's or Master's in Data Science, Statistics, or related fields; programming skills in Python/RBachelor's in Data Analysis, Statistics, or related fields; proficiency in Excel, SQL
Work EnvironmentDeveloping models, advanced analytics, machine learning tasksData cleaning, reporting, basic analysis
Employer & Industry UsageFinancial institutions, e-commerce, insurance

Data Scientist Fraud Detection focuses on building predictive models and applying machine learning techniques to identify fraud patterns. Data Analysts Fraud Detection primarily perform data cleaning, reporting, and basic analysis to support fraud detection efforts. While both roles work in similar industries, Data Scientists handle more complex modeling, whereas Data Analysts focus on data interpretation and reporting.

What are popular job titles related to Data Scientist Fraud Detection jobs in Delaware?

For Data Scientist Fraud Detection jobs in Delaware, the most frequently searched job titles are:

What job categories do people searching Data Scientist Fraud Detection jobs in Delaware look for?

The top searched job categories for Data Scientist Fraud Detection jobs in Delaware are:

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Cities in Delaware with the most Data Scientist Fraud Detection job openings:

Data Scientist Fraud Detection jobs near you

Product Owner with Gen AI

Techridge, Inc.

Wilmington, DE • On-site

Other

Posted 4 days ago

Job description

A Product Owner experienced in managing Generative AI projects in the Credit Card business leveraging Large Language Models (LLMs) and generative techniques to enhance customer experience, automate internal workflows, and optimize fraud detection. Equipped with financial technology, data science, and agile product management to deliver secure, ethical, and high-value AI solutions.

Job Description:

  • 8+ years of relevant experience

Key Responsibilities

  • GenAI Product Strategy: Define the roadmap for adopting Generative AI within credit card domains such as customer support bots, personalized marketing content generation, credit risk assessment, and fraud detection.
  • Backlog Management: Translate business needs into actionable user stories and acceptance criteria for data scientists and engineers.
  • Model Performance & Governance: Own the end-to-end performance of AI models, ensuring accuracy, reliability, and safety (e.g., mitigating hallucinations in LLMs).
  • Ethical & Regulatory Compliance: Ensure all GenAI initiatives comply with financial regulations (e.g., GDPR, data privacy laws) and adhere to internal AI governance frameworks.
  • Cross-functional Collaboration: Work closely with legal, compliance, risk management, and IT teams to ensure secure deployment of AI solutions.
  • Value Optimization: Evaluate the business impact of AI initiatives, measuring KPIs such as reduced customer service costs, higher engagement rates, or lower fraud rates.

Required Skills and Qualifications

  • Experience: 5+ years of experience as a product owner, preferably in financial services, banking, or credit cards.
  • AI/GenAI Knowledge: In-depth understanding of LLMs, prompt engineering, Retrieval-Augmented Generation (RAG), and AI development workflows.
  • Domain Expertise: Knowledge of credit card systems, fraud detection mechanisms, or personalized customer marketing.
  • Technical Literacy: Familiarity with Python, SQL, and data visualization tools, along with an understanding of AI/ML frameworks (e.g., TensorFlow, LangChain).
  • Agile Proficiency: Strong experience in Scrum or SAFe frameworks.

Potential Focus Areas in Card Business

  • GenAI-Powered Support: Developing LLM-powered chatbots that provide instant, human-like assistance for balance inquiries, disputes, and rewards questions.
  • Fraud Detection & Investigation: Using AI to analyze unstructured data for real-time risk assessment and automated fraud investigation reporting.
  • Personalization: Generating personalized rewards offers or financial insights for cardholders.

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