Staff Data Scientist building complex analytics models for underwriting and operations at Travelers Insurance. Collaborating with teams to enhance risk segmentation and streamline decision - making processes.
About the role
- Develop and implement quantitative solutions to improve ability to detect and prevent identity theft, account takeover, and fraud
- Develop and continuously update internal identity theft and authentication models to mitigate fraud losses
- Closely partner with Strategies team, Director of Fraud Identity Analytics and Director of Fraud Model Management on model builds
- Partner with Technology to deploy a Financial Crimes graph database strategy, including vendor selection
- Deploy graph databases and graph techniques to identify criminal networks engaging in fraud
- Generate and prioritize fraud-dense rings to mitigate losses and improve Member experience
- Identify and work with technology to integrate new data sources for models and graphs
- Export insights to decision systems to enable better fraud targeting
- Drive continuous innovation in modeling efforts including advanced techniques like graph neural networks
- Collaborate with the analytics community to share standard methodologies
Requirements
- Bachelor’s degree in mathematics, computer science, statistics, economics, finance, actuarial sciences, science and engineering, or other similar quantitative field; OR 4 years of experience in statistics, mathematics, quantitative analytics, or related experience may be substituted in lieu of degree.
- 6 years of experience in a predictive analytics or data analysis
- 4 years of experience in training and validating statistical, physical, machine learning, and other advanced analytics models.
- 4 years of experience in one or more dynamic scripted languages (such as Python, R, etc.) for performing statistical analyses and/or building and scoring AI/ML models.
- Proven experience writing code that is easy to follow, well documented, and commented where vital to explain logic (high code transparency).
- Strong experience in querying and preprocessing data from structured and/or unstructured databases using query languages such as SQL, HQL, NoSQL, etc.
- Strong experience in working with structured, semi-structured, and unstructured data files such as delimited numeric data files, JSON/XML files, and/or text documents, images, etc.
- Demonstrated skill in performing ad-hoc analytics using descriptive, diagnostic, and inferential statistics.
- Ability to assess and articulate regulatory implications and expectations of distinct modeling efforts.
- Advanced experience with the concepts and technologies associated with classical supervised modeling for prediction such as linear/logistic regression, discriminant analysis, support vector machines, decision trees, forest models, etc.
- Advanced experience with the concepts and technologies associated with unsupervised modeling such as k-means clustering, hierarchical/agglomerative clustering, neighbors algorithms, DBSCAN, etc.
- Experience guiding and mentoring junior technical staff in business interactions and model building.
- Experience communicating analytical and modeling results to non-technical business partners with emphasis on business recommendations and actionable applications of results.
Benefits
- comprehensive medical, dental and vision plans
- 401(k)
- pension
- life insurance
- parental benefits
- adoption assistance
- paid time off program with paid holidays plus 16 paid volunteer hours
- various wellness programs
- career path planning and continuing education assistance
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