Analyst in Actuarial and Data Science

Company:Marsh

Description:

Marsh USA LLC is seeking candidates for the following position based in the New York, NY office (A telecommuting/hybrid work schedule may be permitted within a commutable distance from the worksite, in accordance with company policies.):

Analyst in Actuarial and Data Science

We will count on you to:

Support the development and implementation of actuarial and data science models. Leverage both established actuarial techniques and more recent machine learning and predictive models for applications in commercial insurance and risk management. Consult with clients and colleagues on complex financial and statistical analyses, and develop approaches for new, market-leading analytics-based tools. Develop modeling approaches for implementing new, market-leading analytics-based tools to understand and address risk. Understand business problems to create an approach that starts with determining structured and unstructured data needs and availability. Build Machine Learning models and finalize with results that unlock insight for clients and colleagues. Demonstrate skill in advanced statistical analysis, data mining, and/or research techniques, combined with broader awareness of the business and ongoing research, while functioning in a collaborative role with the Data Science team and across the wider organization. Stay current with ongoing research in the field and bring new approaches to the team. Serve as an internal expert resource and champion for data science. Coach team members on the delivery of analytics-based tools and analyses and presentation of findings.

What you need to have: 

Must have a Master’s degree or foreign equivalent in Analytics, Mathematics, Statistics, Data Science, or a related field plus two (2) years of experience in the position offered, as a Data Analyst, or a related position. Must have two (2) years of experience with all of the following: Predictive modeling using advanced approaches including multiple linear regression, random forest, or neural nets in Python; Cleaning, preparing, and manipulating data for statistical analysis using Python Numpy and Pandas packages; Building models using either PyTorch or TensorFlow Deep Learning frameworks; Connecting to large databases writing SQL queries executed via front-end connections in Excel VBA or Python; Data Visualization and communication to business stakeholders using Power BI, Tableau, or Qlik; Analyzing, quantifying, and communicating financial risk in a corporate setting.

The applicable base salary range for this role is $113,500 to $192,800 per year.

The base pay offered will be determined on factors such as experience, skills, training, location, certifications, and education. Decisions will be determined on a case-by-case basis. In addition to the base salary, this position may be eligible for performance-based incentives.

We are excited to offer a competitive total rewards package which includes health and welfare benefits, tuition assistance, 401K savings and other retirement programs as well as employee assistance programs.

#LI-DNI

Marsh, a business of Marsh McLennan (NYSE: MMC), is the world’s top insurance broker and risk advisor. Marsh McLennan is a global leader in risk, strategy and people, advising clients in 130 countries across four businesses: Marsh, Guy Carpenter, Mercer and Oliver Wyman. With annual revenue of $23 billion and more than 85,000 colleagues, Marsh McLennan helps build the confidence to thrive through the power of perspective. For more information, visit marsh.com, or follow on LinkedIn and X.Marsh McLennan is committed to embracing a diverse, inclusive and flexible work environment. We aim to attract and retain the best people and embrace diversity of age background, disability, ethnic origin, family duties, gender orientation or expression, marital status, nationality, parental status, personal or social status, political affiliation, race, religion and beliefs, sex/gender, sexual orientation or expression, skin color, veteran status (including protected veterans), or any other characteristic protected by applicable law. If you have a need that requires accommodation, please let us know by contacting [email protected].