Address model drift and performance of business requirements as part of recurring operations, drive quantitative model maintenance prioritization, and inform stakeholders.
Understand broad spectrum of Machine Learning and statistical models, impactful features from structured/unstructured data.
Understand and have experience in models that help automation, getting insights, make smart decisions, are able to meet the desired KPIs post-production and predictive modelling in Python, Azure (optional), Insurance(optional)
Stay abreast of the developments in the rapidly changing field of AI and machine learning
Gain deeper understanding of Chubbs business and develop expertise on multiple lines of business
Collaborate with business partners and peers within the organization to understand the machine learning models and develop robust model monitoring solutions that ensure data quality and analytic accuracy.
Execute all aspects of analytics model monitoring including exploratory data analysis, data processing, and model monitoring.




Ability to identify trends and outliers in model results and analyze drift and impact to business value.
Research, recommend, and implement statistical and other mathematical methodologies appropriate for model monitoring.
Create/Maintain excellent working relationships with business partners across the Chubb organization
Provide supporting documentation for the models being monitored including documentation of methodologies used and data issues encountered.

Qualifications
Required:
Some experience utilizing model monitoring methodologies.
Understanding of data mining predictive modeling, and ML concepts/ Probabilistic Models, Ensemble Techniques, Hyperparameter Optimization, machine learning models/sound knowledge of Regression/ Logistic Regression, Random forest, XGBoost, SVM, Clustering, statistical modelling (GLMs in particular), etc.
Proficient in Python with robust capabilities in data analysis/modelling
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📌 Data Scientist Bengaluru (India)
🏢 Chubb Business Services India Private
📍 India