Data Science Director


Date: Dec 12, 2018

Location: New York, NY, US

Company: New York Life Insurance Co

New York Life Investments (NYLIM), an indirect, wholly owned subsidiary of New York Life Insurance Company, is a top 25 global asset management firm. With more than $500 billion in assets under management, NYLIM is a premier investment management firm serving a variety of client segments including retail, institutional, insurance and defined contribution and benefit on a global basis. New York Life Investments offers a diverse set of investment capabilities ranging from traditional equity and fixed income to alternative investment strategies and multi-asset solutions. Renowned for its premier investment acumen and client focus, NYLIM’s vision is to be one of the most trusted providers of investment management expertise and long-term financial security.


NYLIM’s Business Intelligence & Analytics team seeks a Data Science Director to lead the development and delivery of advanced analytics supporting Sales, Marketing, Product and Operations.  This role will build the data science function and partner with senior business stakeholders to deliver innovative, data-driven solutions to solve problems and improve business outcomes across functions.


Primary responsibilities:

  • Launch and manage complex analytic initiatives from project/sample design, business review meetings with internal and external clients deriving requirements/deliverables, reception and processing of data, performing analyses and modeling to final reports/presentations, communication of results and implementation support.
  • Demonstrate to internal and external stakeholders how analytics can be implemented to maximize business benefits. Proactively suggest innovative solutions and provide technical support, which includes strategic consulting, needs assessments, project scoping and the preparation/presentation of analytical proposals.
  • Utilize advanced statistical techniques (e.g. ML, AI) to create high-performing predictive models and deep analyses to address existing challenges to sales & marketing effectiveness and product design.
  • Partner with IT to design and develop a scalable data analytics platform for model development and data visualization.
  • Build and mentor a team of Data Scientists and Data Engineers.
  • Facilitate consensus and balance the needs of many different constituencies.  Manage multiple tasks and projects simultaneously, establish priorities, and own deliverables end to end.
  • Actively contribute to analytics strategy by proposing ideas, preparing presentation material for internal stakeholders, and product design/business case materials for NYLIM leadership.


Qualifications and skills:

  • Graduate-level degree required, with concentration in a quantitative discipline such as statistics, computer science, mathematics, economics, or operations research.
  • 7+ years of hands-on work experience developing and applying predictive models and other advanced statistical approaches in a business setting, preferably in a marketing and sales context.
  • 3+ years’ experience working with Hadoop ecosystem with frameworks like MapReduce, Hive, Pig, Spark, Impala, etc.
  • Applied expertise in statistical modeling techniques such as linear regression, logistic regression, GLM, tree models (e.g. Random Forests and GBM), cluster analysis, principal components. Experience with feature creation, variable selection and model validation.
  • Strong background in analytic programming (e.g. R, Python, SAS,) and data manipulation (e.g. SQL).
  • Familiarity with Artificial Intelligence, Machine Learning, and Natural Language Processing open source libraries, such as NumPy, Pendas, Scikit-learn, TensorFlow, …
  • Experience with data visualization (e.g. R Shiny, D3, Tableau).
  • Team/people management experience.
  • Asset management industry experience is a plus.
  • Ability to build relationships and work effectively with diverse stakeholders at all levels.
  • Strong written and verbal communication skills.
  • Ability to work efficiently in a fast-paced environment.





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