Continental Western Group and Berkley Agribusiness are part of the W.R Berkley Corporation, the nation’s premier commercial lines property casualty insurance provider. Each of the operating units in the Berkley group participates in a niche market requiring specialized knowledge about a territory or product. Our competitive advantage lies in our long-term strategy of decentralized operations, allowing each of our units to identify and respond quickly and effectively.

The Sr. Data Scientist may be responsible for leading or participating in the cross departmental collaboration to define and develop predictive modeling initiatives and building, refining, and improving existing models as well as developing new techniques and applications to different areas of the company.  This position leads or participates with cross-departmental collaboration and communication of complex technical models as well as leads or participates with the implementation of the models on behalf of Actuarial & Advanced Analytics including interfacing with IS/IT and Underwriting business rules and others. 

• Leads or participates in the cross departmental collaboration to define and develop predictive modeling initiatives, communication of complex technical and operational model solutions and the implementation of model solutions.  Leads and helps shape the overall advanced analytics strategy for the organization.
• Pro-actively seeks out situations where the acquisition of Competitive Market Analytics and other competitive position reviews would have a material impact on business decisions and their outcomes.  Convinces others of the need and lead the effort.
•  Adapts and develops rating methodologies for both existing and new company products using advanced techniques to incorporate new variables, etc., in pursuit of a competitive advantage and creation of economic profit opportunities.
•  Identifies, retrieves, and prepares data in support of actuarial analysis through SQL, Essbase, and other data sources.  Includes internal data (exposure, premium, loss, claims, price, etc.) and various forms of external data (bureau loss costs, bureau trends, macroeconomic data, market share, market pricing, etc.)
•  Ensures the accuracy and suitability of data for the business need at hand.  Manipulates data, performs preliminary analysis and interprets data through Excel or other analytics platforms and presents recommendations.
•  Supports the transformation of business intelligence tools from static reporting to Tableau dashboards, increasing data visualization and availability, supporting cross-division collaboration, and operationalizing advanced analytics.
•  For both Home Office and Regional Underwriting, identifies segments with profitable growth potential; identifies under-performing segments and proposes corrective actions to improve profitability.
•  Performs advanced analytics and modeling techniques including but not limited to generalized linear modeling and other forms of multivariate analysis.  Tools used could include SAS, R, Python, and distribution fitting coupled with stochastic simulation techniques.  This role is expected to build, refine and improve existing models as well as develop new techniques and applications to different areas of the company.
•  In additional to regular modeling responsibilities, this person will use critical thinking skills and apply advanced modeling techniques, including but not limited to Machine Learning, to provide insights and solve business problems including techniques such as decision trees, A/B testing, random forests, neural networks, clustering, and other machine learning techniques.
•  Participates in enterprise risk management initiatives, including support for catastrophe management, economic capital modeling, internal rate of return studies, etc.  Provides innovation and leadership when applicable.
•  Leads the innovation and efficiency efforts related to tools and analysis in support of Underwriting’s pricing decisions with respect to large accounts as well as small commercial portfolio price adequacy analyses.

• Master Degree in Math, Statistics, Applied Mathematics, or similar studies from a university
• 5  years related experience with large data sets analytical problem solving in a business environment, or equivalent combination of education and work experience is required.
• Experience with R, Python, SAS, SQL in developing predictive models, including at least one of Javasript, C#  or other web development tools. 
• Experience in building web applications as a workflow business integration product is a plus.