Joslyn Lim

基于机器学习回归模型的电机备件价格估算方法, accelerate the claim processing time, and systematize the part price assessment procedure. Collaborating with business users and the AWS team, 我从用户角色开始, 过程的理解, 头脑风暴会议. 该团队设计了一个解决方案，将机器学习与运行在AWS架构上的web应用程序结合起来，为不同汽车品牌和型号的备件推荐预测价格. The final model used was ensemble gradient boosted trees (GBT) regressors. 这一举措预计将提高客户满意度，因为它还缩短了索赔处理时间，减少了通货膨胀对备件成本的影响.

一种分类机器学习模型，预测哪些政策可能会在即将到来的更新周期中停止. Regional retail general insurance has a very competitive landscape, so it is imperative to monitor a churn rate and keep existing customers whenever required. 开发团队与业务用户合作，了解在客户流失预测中有用的基本特性. As part of the data scientists team, I used past years' data to support the claims. The team built a random forest (RF) model to help end users retain the fence-sitters, i.e., indifferent customers and likely-to-churn customers, with incentives or targeted promotions.

The project was designed to predict the named entity, document sentiment and entity sentiment, locality, 和情感，准确度更高. In 2019-2020, Bert was state of the art (SoTA) for NLP. Still, it was not giving satisfying results for ASEAN languages (e.g., Indonesian, ASEAN Chinese, Malay, etc.) as the underlying models were using mostly US corpus. The team aimed to beat the SoTA for ASEAN languages. 从数据收集, curation, annotation, QA, 数据清理, 模型训练, evaluation, 和测试, the team of 10 was doing end-to-end delivery for our client. 我是其中一个语言支柱的所有者，同时也帮助其他语言支柱理解区域语言的上下文. 该团队进行了多次机器学习实验，发现变压器模型对一些语言语料库和它们的混合物效果最好, hence applying the large embedding to multiple language models. As the end deliverable, the team containerized the models and Python pipeline using Docker. The models were able to beat SoTA with around a 0.7 F1 score.

一个区域外包和共享服务在过去一年经历了很高的员工流失率，希望减少人员流失, especially for the higher performer. 一个由五人组成的团队进行了定性和定量分析，以了解员工的痛点，并向客户提出了解决方案. I acted as a data scientist to conduct exploratory analytics using employee attendance and leave, 考核与绩效, 奖金和薪酬, and keyword extraction from interview sessions using Python, R, 和Power BI. 然后，将这些见解与流失数据相匹配，通过分类算法预测流失可能性，并总结(聚类和PCA)流失因素，以便客户能够优先考虑并针对影响较大的流失因素采取行动.