Peter Chen Email and Phone Number
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．Strong domain knowledge in the Insurance industry (Auto Underwriting)．Skills in statistical analysis using Python, R, and SAS programming with large datasets．Experiences in machine learning (logistic regression, random forest, gradient boosting, etc)- CORE COMPETENCIES．SOA Exams Passed: P, FM．Programming & Tools: R, Python, PySpark, SAS, SPSS, EXCEL, Power BI, Tableau, SQL．SAS Certified Base Programmer for SAS 9 | SAS EG．Machine Learning Algorithms: GLM, Random Forest, Gradient Boosting (XGBoost, CatBoost), SVM, Clustering (Hierarchical Clustering, K-means Clustering)．Statistics knowledges: Regression Analysis, Time Series Analysis, Multivariate Analysis, Survey Sampling, Nonparametric Statistics, Big Data Analytics, Statistical Data Analysis
Lexisnexis Risk Solutions
View- Website:
- risk.lexisnexis.com
- Employees:
- 9577
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Lexisnexis Risk SolutionsAtlanta, Ga, Us
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Lead Data ScientistLexisnexis Risk Solutions Apr 2024 - PresentAlpharetta, Ga, Us• Architected the data strategy roadmap of Insurance Performance Datamart for data science team, aligning technical solutions with business goals, enhancing cross-team collaboration, and ensuring impactful, data-driven outcomes.• Direct the development of the Prospect Survival Score model to predict retention risks, providing technical expertise across the full modeling life cycle while mentoring senior members to deliver a robust, high-performing pipeline.• Led the end-to-end development of a household-level Lifetime Value model using XGBoost, guiding the team through model design, feature optimization, and implementation, resulting in a 7x lift in predictive power.
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Senior Data ScientistLexisnexis Risk Solutions Jul 2022 - Apr 2024Alpharetta, Ga, UsAuto Underwriting/Vehicle Build - Advanced Driver Assistance Systems (ADAS)• Conducted a study on the risk performance associated with claims from unlisted drivers, improving the loss indicator to assist insurance carriers in more accurately identifying high-risk policyholders.• Researched the insured electric vehicles and to analyze the rate of switching from Internal Combustion Engine (ICE) to other electric vehicle types and to assess the subsequent loss performance of policyholders who switched between automobile types• Conducted multivariate analysis to determine the pure impact of the combinations of different core ADAS features and create significant pricing segmentation for vehicles using GLM and tree-based models
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Data Scientist Ii, DsrpLexisnexis Risk Solutions Jul 2020 - Jul 2022Alpharetta, Ga, UsLife, Batch, A&R, Auto• Developed enhanced Pool Adjacent Violators Algorithm and automatic Python scoring programs by leveraging Master Model Framework for variable treatments, GLM, and model assessment• Built an annual $10+MM revenue semi-automated process with multiple combined products to help a carrier understand their monthly auto insureds' portfolios and find additional premiums using Python• Conducted XML data transformation, validated the match for Life Risk Classifier (LRC), and analyzed MVR violations of LRC production population by score binning to improve a critical business process with less costs in Python
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Graduate Research AssistantGeorgia State University - J. Mack Robinson College Of Business Aug 2018 - May 2020Atlanta, Georgia, Us• Conduct fuzzy join on unstructured datasets of governmental bonds, winsorize outliers, and model order logistic regression analysis using SAS and R -
Modeling & Analytics InternLexisnexis Risk Solutions May 2019 - Jul 2019Alpharetta, Ga, Us• Automated statistical visualization to self-serve interactive exploration in Power BI and assisted business people to better understand data in greater efficiency• Enhanced predicted solutions to improve the efficiency of auto insurance quoting process for a top carrier with logistic regression, random forest, and gradient boosting for features selection in Python
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Actuarial AnalystChubb Feb 2017 - Jun 2018Ch• Built models to monitor loss ratio of Taiwan Mobile Phone Insurance and project ultimate claims and earned premium for six channels with more than 3GB raw datasets in R and SAS programming.• Analyzed insured’s preference, potential customers, and risk probability for Travel Accident Insurance using R with semi-structured data of more than 5 million observations.• Delivered loss ratio report of Group Personal Accident for each benefits with semi-structured data in VBA.• Conducted Own Risk and Solvency Assessment (ORSA) report of Taiwan branch with global headquarters’ unit members and participated in ERM system launching and risk assessment. -
InternO-Bank 王道銀行 Aug 2012 - Aug 2012內湖區, 台北市, TwPrevious: Industrial Bank of Taiwan (2897)• Constructed 2 financial datasets and supported weekly financial reports. -
InternFubon Financial Holding Co., Ltd. 富邦金控 Jul 2012 - Jul 2012Taipei City, Tw• Supported data analysis with descriptive statistics and data collection on London InterBank Offered Rate.
Peter Chen Skills
Peter Chen Education Details
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Georgia Tech Scheller College Of BusinessMaster Of Business Administration - Mba -
Georgia State University - J. Mack Robinson College Of BusinessActuarial Science
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Georgia State University - J. Mack Robinson College Of BusinessQuantitative Risk Analysis And Management
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National Chengchi UniversityStatistics
Frequently Asked Questions about Peter Chen
What company does Peter Chen work for?
Peter Chen works for Lexisnexis Risk Solutions
What is Peter Chen's role at the current company?
Peter Chen's current role is Lead Data Scientist at LexisNexis Risk Solutions | MBA Candidate at Georgia Tech.
What is Peter Chen's email address?
Peter Chen's email address is pe****@****isk.com
What schools did Peter Chen attend?
Peter Chen attended Georgia Tech Scheller College Of Business, Georgia State University - J. Mack Robinson College Of Business, Georgia State University - J. Mack Robinson College Of Business, National Chengchi University.
What skills is Peter Chen known for?
Peter Chen has skills like Leadership, Presentation Skills, R, Data Science, Insurance, 資料模型化, Predictive Modeling, Object Oriented Programming, Microsoft Powerpoint, 機器學習, Data Analysis, Business Analysis.
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