Analyst - Data & BI

The Purpose of the role The Risk Information Analyst role will involve monitoring, investigating, and reporting trends across multiple industrial sectors and market segments to evaluate specific known and trending emerging risks and unknown risks that may negatively impact one or more Chubb product lines. Areas of research will include a global focus on legal, political, economic, social, technological and environmental issues affecting the insurance industry. Responsibilities Undertake industry or subject-specific research as required. Leverage data from a variety of sources to analyze risk trends and impact and provide insights to senior management. Provide reports on the progression, scope, and impact of an emerging risk. Investigate and collect new sources from legal databases, regulatory bodies, government institutions, news and science aggregators, etc. Perform quality check (including editing, formatting, and providing source). Required knowledge, Skills and qualifications with gathering, cleaning, interpreting and analyzing data using statistical techniques preferred. Ability to write concise, logical, and grammatically correct reports with scraping web content using open-source or commercial tools and ability to learn any new web tools Ability to deep dive to understand the source of new emerging risks and monitor their progression. High level of expertise with Microsoft Office applications, especially Word, PowerPoint and Excel Well-rounded, quick and eager learner Flexible in approach and able to work well with tight deadlines Able to work remotely with minimal supervision Strong communication skills An analytical mind with problem-solving abilities Applicants with a B.S., B. Tech, B.Sc., M. Tech, or M.Sc. degree will be considered. Applicant with business and/or technical degrees are preferred. Ability to use data visualization tools to identify trends, patterns, and outliers within large data sets would be a value add Knowledge of Python and understanding text representation techniques (such as n-grams, bag of words, sentiment analysis etc.), statistics and classification algorithms preferred.