Machine Learning Engineer

About the Company

The Purpose of the role

We are seeking a Senior Data Scientist for the Advanced Analytics team, who will be at the forefront of developing new innovative data driven solutions with cutting edge machine learning algorithms. As a Senior Data Scientist, you will build machine learning models, perform quantitative data analysis to enable organizational decision making and develop solutions to complex business problems and create value for the business. This position offers exposure to a wide variety of analytics tools and technologies as well as unique challenges in problem-solving. Building and deploying predictive models in support of our pricing strategy will be a focus area. The ideal candidate will possess a strong background in statistical modeling, machine learning, and data analysis. In this role, you will collaborate with cross-functional teams to extract insights from complex data sets, develop predictive models, and implement innovative solutions that enhance business performance.

About the Role

We are seeking a Senior Data Scientist for the Advanced Analytics team, who will be at the forefront of developing new innovative data driven solutions with cutting edge machine learning algorithms.

Responsibilities

• Work with business partners globally, manage deliverables against timelines, presentation of results and implement the model.
• Synthesize data to uncover inherent trends, assess impact of data on business usage, and to make recommendations for improvement.
• Build GLM pricing models/machine learning models in support of various property and casualty insurance products.
• Research, recommend, and implement statistical and other mathematical methodologies appropriate for the given business problem.
• Build explanatory and forecasting models using descriptive and inferential statistics, and regression analysis.
• Perform data validation, preprocessing, and exploratory data analyses.
• Communicates analytical goals, approaches, outcomes, and insights to both technical and non-technical audiences.
• Collaborate with business partners and peers within the organization to understand and scope the problem, gather business requirements, and develop robust model solutions that drive improvement in key business metrics.
• Effectively communicate with key stakeholders (both technical and non-technical) in written, oral and presentation formats.
• Create/Maintain excellent working relationships with business partners across the Chubb organization including Product, Actuarial, IT, and analytics peer groups.
• Develop adaptable modeling frameworks and reusable assets that can be applied across various projects.

Qualifications

• Master's degree in Statistics, Mathematics, Analytics, or Computer Science is preferred.

Required Skills

• Sound understanding of ML concepts/ Probabilistic Models, Supervised / Unsupervised Learning, Ensemble Techniques, Hyperparameter Good knowledge of constraint Optimization, and Deep Learning; Strong experience in developing and maintaining machine learning models/sound knowledge of GLM, Regression/ Logistic Regression, Random Forest, XGBoost, SVM, Clustering, building data pipelines in Azure/Databricks.
• Proficient in Python/Pyspark with strong capabilities in data analysis/modelling.
• Explore data and communicate insights clearly to non-technical as well as technical audience.
• Expertise in python machine learning tools and libraries (scikit-learn, h2o, R/MLlib or similar etc.)
• 4-6 years of work experience in building models is required.

Preferred Skills

• Desirable to have experience in visualization tools like Qlik, Power BI, Tableau etc.
• Desirable to have experience with data and machine learning services using Azure.
• Desirable to have knowledge/ experience in big data technologies.
• Must have strong problem-solving skills.
• Should be adept at research and must look to apply new methodologies to varied situations.
• Experience in Actuarial Science and Insurance will be an advantage.
• Must possess an advanced degree in engineering/computer sciences/data science/business analytics/statistics/mathematics or passed actuarial exams.

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