Director, Data Science (The Prudential Insurance Company of America, Newark, NJ): Develop cutting edge solutions to complex business problems using machine learning, statistical and mathematical modeling, and advanced analytics. Formulate, develop and apply advanced statistical and machine learning models for prediction and optimization, including a wide variety of machine learning technologies (logit, regression, decision trees/forests, boosted models, clustering). Develop relationships with business and technical leaders and influence the adoption of data science solutions for business problems and opportunities. Build and execute a portfolio of initiatives that drive meaningful impact for marketing and sale organizations. Identify opportunities to use data science to drive cost reduction, increased revenue, and improved customer experiences. Identify, integrate and mine large data sets, connecting data from disparate sources to identify insights and patterns using traditional as well as predictive and prescriptive analytics. Oversee the creation, implementation, and delivery of an end-to-end technical solution for a data science project. Conduct statistical analysis using large structured and unstructured data sets (speech analytics, digital footprints, customer behaviour, financial information, proprietary market research, and secondary sources) with the goal of discovering meaningful implications for business decisions. Identify, source, transform and join public, proprietary and internal data sources. Model large structured and unstructured data sources (financial transactional, time-series, text, speech/audio and image). Deliver presentations, benefit analysis, reports, and other deliverables to communicate findings key stakeholders and make recommendations with a focus on “what it means” and “actions” to consider. Manage and mentor a team of junior staff in applying theory to practice on a day-to-day basis in business context, anticipate roadblocks, and represent the technical side of the project at all meetings. Full time employment, Monday - Friday, 40 hours per week. Telecommuting permitted up to 3 days per week.MINIMUM REQUIREMENTS:Master’s degree in Mathematics, Statistics, Engineering, Econometrics, Physics, Computer Science, Actuarial, Data Science, or a related quantitative field, and 2 years of experience in developing advanced quantitative, analytical, and statistical solutions; OR a PhD in Mathematics, Statistics, Engineering, Econometrics, Physics, Computer Science, Actuarial, Data Science, or a related quantitative field, and 1 year of experience in developing advanced quantitative, analytical, and statistical solutions. Of the required experience, must have 1 year of experience in each of the following: •Applying statistical machine learning techniques and Artificial Intelligence (AI) techniques: logit, regression, random forests, boosted machines, clustering, LSTM, large scale language models including BERT, deep learning, optimization, convolution neural networks, and customer lifetime value; •Spanning analysis of real-world problems involving structured and unstructured data; •Utilizing tools and programming languages for statistical modeling of large data sets: SQL, AWS SageMaker, and Python; •Productionizing models in the cloud environment: AWS or Microsoft Azure; •Applying the principles of Multithreading, data structures and algorithms; and •Software engineering experience in Java, Python, or Scala languages. Telecommuting permitted up to 3 days per week. Domestic travel required up to 10%.To Apply: Apply online at "https://bit.ly/3SuZZ8V". Should you have any difficulty in applying for this position through our website, please contact [email protected] for assistance in the application process.