Zurich NA Data Science Intern, Jersey City (Summer 2020) in Jersey City, New Jersey
Data Science Intern, Jersey City (Summer 2020)
Zurich is currently looking for a Data Science Intern to join the Data & Analytics Team to work out of our Jersey City office.
Zurich’s Data & Analytics team derives insights from a wide range of proprietary and external aggregated data to identify future risks and inform strategic decision-making. Our methods provide a deeper and broader analysis of catastrophic damage and hard-to-predict events — those of low frequency but high severity. Proactive solutions can also help organizations reduce their total cost of risk and create more effective strategies.
Zurich’s Data & Analytics team makes a real difference to how Zurich and Zurich’s customers manage risk. The Data & Analytics team uses their insurance expertise, data and advanced analytics to solve real business problems.
As a Data Science Intern, you will:
Work with a project team to solve business problems. Examples of what we do include: build models to set the appropriate price for various insurance products, predict claims that will cost over $100,000, detect fraud and predict crop yields for crop insurance.
Improve and gain understanding of Data & Analytics techniques such as,data mining, deep learning and other machine learning modeling methodologies
Use Python, R, SQL languages as well as Jupyter, ArcGIS, H2O, Spark and Github tools.
Learn from our summer training program covering insurance basics and advanced machine learning methods.
We have a collaborative and inquisitive environment where you can apply educational knowledge to create insights to impact change for current business problems while gaining mentorship and hands-on training from experienced data scientists.
Actively pursuing a Bachelor’s degree
Currently enrolled in a Bachelors or Masters program and will be enrolled in the fall of 2020
Two or more years of college coursework (should be a rising Junior or Senior) and no prior years of experience required in the Administrative area
Minimum cumulative current grade point average of 3.0 (current could mean last semester GPA)
Master’s degree in Statistics, Mathematics, Computer Science, Engineering, or other relevant STEM degree.
Current enrollment in a graduate level program
Demonstrated project experience including the use of statistical techniques such as regression, generalized linear modeling (GLM), econometrics, machine learning, decision trees, clustering, and neural networks
Ability to apply statistical methodology to solve business problems
Problem solving skills including identification of issues and offering solutions
Experience with programming and data tools such as R, Python, Hadoop, ArcGIS, H20, Spark, Github, etc.
Strong written and verbal communication skills; experience summarizing and presenting information in a way that provides clarity and interest
Self-motivated and able to work independently in support of project and team goals
Imagine working for a company that truly cares about their employees, customers, stakeholders, and communities they serve.
Imagine working for a values-driven organization that has the ambition and desire to be the best global insurance provider in the world.
Zurich is that place where 55,000 employees across 200 countries and territories are all focused on helping people and helping companies protect what is truly most important to them. We are a values-driven organization that takes pride in the work that we do every day and we have the ambition to be the best global insurer in the world.
EOE Disability / Vets
Zurich does not accept unsolicited resumes from search firms or employment agencies. Any unsolicited resume will become the property of Zurich American Insurance. If you are a preferred vendor, please use our Recruiting Agency Portal for resume submission.
Primary Location: United States-New Jersey-Jersey City
Relocation Available No
Job Posting 10/14/19
Unposting Date Ongoing
Req ID: 190007M0
It is the Policy of Zurich in North America, as an equal opportunity employer, to attract and retain the best-qualified individuals available, without regard to race/ethnicity, color, religion, gender expression, genetic information, national origin, sex, gender identity, sexual orientation, marital status, age, disability or protected veteran status.