Fraud Detection Associate jobs in Texas

Position: Data Scientist - Handwriting Analysis for Fraud Detection

Location: Richardson, TX (Onsite, M-F)

Summary:

We are seeking a highly skilled Data Scientist with expertise in handwriting analysis, particularly in the context of fraudulent checks. The ideal candidate will possess a deep understanding of handwriting patterns, graphology, and machine learning techniques to detect anomalies and patterns indicative of fraudulent activities. This role requires a strong background in data analysis, statistical modeling, and programming skills to develop and deploy advanced algorithms for fraud detection.

Responsibilities:

1. Analyze and interpret handwriting samples from checks to identify patterns, anomalies, and potential signs of fraudulent activity.
2. Develop and implement machine learning algorithms and statistical models for automated handwriting analysis and fraud detection.
3. Collaborate with cross-functional teams, including fraud investigators and software engineers, to integrate handwriting analysis solutions into fraud detection systems.
4. Conduct research to stay abreast of advancements in handwriting analysis techniques and incorporate relevant methodologies into existing processes.
5. Utilize data visualization techniques to present findings and insights to stakeholders, enabling informed decision-making.
6. Participate in the development and enhancement of data collection processes to improve the quality and quantity of handwriting samples for analysis.
7. Stay informed about industry regulations and standards related to check fraud detection and ensure compliance with applicable laws and guidelines.
8. Provide expertise and guidance to other team members on handwriting analysis techniques and best practices.

Qualifications:

1. Master's or Ph.D. degree in Computer Science, Statistics, Mathematics, or a related field with a focus on machine learning, handwriting analysis, or a related discipline.
2. Proven experience in handwriting analysis, graphology, or document examination, preferably in the context of fraud detection.
3. Strong proficiency in programming languages such as Python, R, or MATLAB, and experience with relevant libraries and frameworks for machine learning and data analysis.
4. Experience with data preprocessing techniques, feature engineering, and model evaluation in the context of handwriting analysis.
5. Excellent analytical and problem-solving skills, with the ability to think critically and creatively to solve complex problems.
6. Effective communication skills, with the ability to convey technical concepts to non-technical stakeholders and collaborate effectively with cross-functional teams.
7. Strong attention to detail and a commitment to maintaining the highest standards of accuracy and integrity in data analysis and interpretation.
8. Ability to work independently and manage multiple tasks simultaneously in a fast-paced environment.

Preferred Qualifications:

1. Experience working in the financial services industry, particularly in the area of fraud detection or risk management.
2. Familiarity with image processing techniques and tools for digital image analysis.
3. Knowledge of deep learning algorithms and frameworks for image recognition and pattern recognition.
4. Experience working with large-scale datasets and distributed computing platforms such as Hadoop or Spark.

Join us in our mission to combat fraud and protect the integrity of financial transactions through cutting-edge handwriting analysis and machine learning techniques.