Job Details

Role:CVP Data Science - Marketing Loc:New York City, NY

Client, A top player in annuities, long-term care and mutual funds, is seeking a Lead Data Scientist in its Center for Data Science and Analytics.
We have a wealth of internal information on consumers, policies and their performance, as well as applicants, prospects and our 10,000 agents. We also have a multitude of external data from a great variety of sources. Analytical challenges range from consumer analytics (segmentation, response, conversion, retention, up-sell), media mix, mortality risk to agent recruiting decisions, fraud detection and digital analytics.
The Center for Data Science and Analytics is the innovative corporate analytics group within company. We are a rapidly growing entrepreneurial department which aims to design, create and offer innovative data-driven solutions for many parts of the enterprise. We explore external data sources and new statistical/machine learning techniques and deliver a whole new generation of analytical solutions.
We work with big data ranging from demographics, credit and geo data to detailed medical data and social media information. We have a modern computing environment with a suite of data science, modeling tools and packages, and a group of talented professionals at various levels to support you. Life insurance is on the verge of huge change. This is a chance to be part of, actually to drive, the transformation of an industry. Is this not why we became data scientists?

You will apply your data and coding skills to ingest, wrangle and explore external and internal data to gain business insights, prepare data for modeling, build and validate predictive models, and support production deployment of models.

Responsibilities

• Independently leads
  
• Proactively demonstrates to internal and external stakeholders how analytics can be implemented to maximize business benefits. Provides technical support, which includes strategic consulting, needs assessments, project scoping and the preparation/presentation of analytical proposals
  
• Mentor/advise Sr Data Scientists on different data wrangling tools and advanced statistical/machine learning techniques to improve predictive models and create actionable insights to address business objectives and client needs
  
• Leads analytical innovation efforts to test and recommend new advanced analytical methods, software and data sources. Shares knowledge and trains others on its adoption within Analytics group
  
• Actively contributes to analytics strategy by contributing ideas, preparing presentation material for internal stakeholders, and product design/business case materials for company leadership
  
• Proactively and effectively communicates with internal stakeholders on product design, data specification, model implementations, with partners on collaboration ideas and specifics, with clients and account teams on project/test results, opportunities, questions
  
• Creates project plans to ensure projects are completed on time and within budget. Provides high quality ongoing customer support, answering questions, resolving issues and building solutions. Proactively identifies potential risk/hurdles and works to remove potential issues
  
• Assures compliance with regulatory and privacy requirements during design and implementation of modeling and analysis projects
  
• Follows industry trends and related data/analytics processes and businesses. Attends conferences, events, and vendor meetings as needed. Functions as the analytics expert in meetings with other internal areas and external vendors.

Required Qualifications

• 10+ years of experience with statistical modeling using large and complex datasets in business setting
  
• 5+ years of experience in the insurance industry (life, health or P&C) or in data-based marketing areas
  
• Strong verbal and written communications skills, listening and teamwork skills, and effective presentation skills
  
• Strong expertise in design of experiments and its applications in business setting
  
• Strong expertise in statistical modeling techniques such as linear regression, logistic regression, survival analysis, GLM, tree models (Random Forests and GBM), cluster analysis, principal components, feature creation, and validation
  
• Strong expertise in statistical sampling, regularization techniques (Ridge, Lasso, elastic nets), variable selection techniques, feature creation and transformation, and model validation (hold-outs, CV, bootstrap)
  
• Familiar with Git version control tool
  
• Experience with data visualization a plus (e.g. R Shiny, Tableau)
  
• Proficiency in creating effective and visually appealing PowerPoint presentations