Giridhar Rajan
Angestellt, Data Scientist, Chubb
Chennai, Indien
Über mich
Summary Total 7 years of experience of which 4.5 extensive experience in Data science, Machine learning, NLP. Machine Learning Solutions Associate with extensive experience in driving growth, KPI and revenue using machine learning, actionable insights, predictive analytics, informed decision making, customer, product analytics, IoT, Text analytics, NLP, social media analytics, Prioritizes collaboration with other scientists to create information that is tested and accurate and also as useful and detail as possible.Had delivered project based on NLP(EntityMatching,Text Classification,) Skills: Python, Machine learning, Deep learning, Transformers, NLP, BERT, Sentence transformers, text mining , text similarity, Aws lambda, Aws sagemaker, Clustering, Regression, SQL, Data mining
Werdegang
Berufserfahrung von Giridhar Rajan
Bis heute 2 Jahre und 8 Monate, seit Nov. 2021
Data Scientist
Chubb
Total 7 years of experience of which 4.5 extensive experience in Data science, Machine learning, NLP. Machine Learning Solutions Associate with extensive experience in driving growth, KPI and revenue using machine learning, actionable insights, predictive analytics, informed decision making, customer, product analytics, IoT, Text analytics, NLP, social media analytics.
Bis heute 2 Jahre und 8 Monate, seit Nov. 2021
Data Scientist
Chubb
2 Jahre und 11 Monate, Jan. 2019 - Nov. 2021
Data Scientist
Tata Consultancy Services Ltd
Machine Learning, Natural Language Processing, Artificial Intelligence, spacy Machine learn, Learning Pattern, Learning Domain based rules, Machine to identify patterns.SVM. Role Description: Machine Learning, Natural Language Processing, Artificial Intelligence, Neural Networks, Machine learn, Learning Pattern, Learning Domain based rules, Project: Sentiment analysis of the agent. * Classification of comments based on business problems.
Objective: Pin codes having similar property are clubbed together into a cluster Approach: K-means algorithm (Distance measure-Euclidean distance) Summary Attributes for this exercise are collected from different sources across PAN America(over 1000 plus pin codes). Some of them are drive time, Traffic density, demographic variables, affluence level etc.. using k-means cluster all the pin co
Sprachen
Englisch
Fließend