Director, Data Scientist

Job Classification:

Technology - Data Analytics & Management

Are you interested in building capabilities that enable the organization with innovation, speed, agility, scalability and efficiency? The Global Technology team takes great pride in our culture where digital transformation is built into our DNA! When you join our organization at Prudential, you’ll unlock an exciting and impactful career – all while growing your skills and advancing your profession at one of the world’s leading financial services institutions.

As a Director, Data Scientist supporting Group Insurance you will partner with Machine Learning Engineers, Data Engineers, Data Analysts and other professionals to build models to support Life Data Analysis.  You will implement machine learning models that will deliver stability, producibility, scalability and integration with other products and services. You will implement capabilities to solve sophisticated business problems, deploy innovative products, services and experiences to delight our customers! In addition to deep technical expertise and experience, you will bring excellent problem solving, communication and teamwork skills, along with agile ways of working, strong business insight, an inclusive leadership attitude and a continuous learning focus to all that you do.  

Here is what you can expect in a typical day:

• Provide deep technical leadership to a portfolio of high impact data science initiatives.  Identify the optimal sets of data, models, training, and testing techniques required for successful product delivery. Remove complex technical impediments.
• Manage team members in data analysis and model development, testing, training, and tuning.  Sometimes apply hands-on experience to ensuring best-in-class model development.  Mentor team members in technical skill development.
• Communicate clearly and concisely, in writing and verbally, all facets of model design and development.  Continuously look for insights in models developed and generate new ideas for model improvement.
• Manage external vendors in the execution of parts of the data science development process.
• Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code
• Bring a deep understanding of relevant and emerging technologies, give technical direction to team members and embed learning and innovation in the day-to-day  
• Work on significant and unique issues where analysis of situations or data requires an evaluation of intangible variables and may impact future concepts, products or technologies 
• Use programming languages including but not limited to Python, R, SQL,  Java or Scala, SQL

The Skills and expertise you bring:

• Masters (Ph.D. preferred) in Statistics, Mathematics, Physics, Computer Science, or Engineering or experience in related fields with deep knowledge of statistical theory.
• Ability to lead independently with minimal guidance and effectively leverage diverse ideas, experiences, thoughts and perspectives to the benefit of the organization  
• Experience with agile development methodologies and Test-Driven Development (TDD)
• Knowledge of business concepts, tools and processes that are needed for making sound decisions in the context of the company's business
• Ability to learn new skills and knowledge on an on-going basis through self-initiative and tackling challenges
• Excellent problem solving, communication and collaboration skills

Significant experience and/or deep expertise with several of the following:

• Statistics and Computing (Preferred): Strong knowledge of: Linear Models, Survival Analysis, Multivariate Statistics,  Function Convergence Properties, Asymptotic Theory, Probability, Statistics, Applied Probability, Applied Statistics, Multivariable Calculus, Linear Algebra, Computer Science (Programming Methodologies), and Cloud.
• Data Acquisition and Transformation: Acquiring data from disparate data sources using API's, SQL and NoSQL.  Transforming data using SQL, NoSQL, and Python.  Visualizing data using a diverse tool set including but not limited to Python and R.  
• Database Management System:
  • Knowledge of how to work with data from (do not build)
  • SQL skills (relational) - CORE / Initial Proficiency
  • Unstructured (NoSQL)
  • Graph / ontology (DB Graph)
• Data Analysis and Insights: Analyzing structured and unstructured data using data visualization, manipulation, and statistical methods to identify patterns, anomalies, relationships, and trends.
• Machine Learning: Deep understanding of machine learning theory, including the mathematics underlying machine learning algorithms.  Expertise in the application