Senior Data Scientist

Overview

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 Senior Data Scientist supporting Group Insurance in the GRI (Global Retirement & Insurance) Technology organization, you will partner with our diverse team of Engineers, Economists, Computer Scientists, Mathematicians, Physicists, Statisticians and Actuaries tasked with mining our industry-leading internal data to develop new analytics capabilities for our businesses. The role requires a rare combination of sophisticated analytical expertise; business acumen; strategic mindset; client relationship skills, problem solving; and a passion for generating business impact.

This role is based in our office in Newark, NJ or Portland, ME. Our organization follows a hybrid work structure where employees can work remotely and from the office, as needed, based on demands of specific tasks or personal work preferences. This position is hybrid and requires on-site presence on a reoccurring weekly basis at least 3 days per week.

Responsibilities

• Responsible for the hands-on development of sophisticated data science solutions comprising the portfolio developed by the Lead Data Scientist and Actuaries and the technical requirements specified by the Lead Data Scientist and Actuaries.
• Perform hands-on data analysis, model development, model training, model testing, and model deployment.
• Continuously research new methods for problem solution, including new algorithms, modeling techniques, and data analytics techniques.
• Write production-level code and partner with machine learning engineers to push development code into production.
• Partner with machine learning engineers to productionize machine learning models. Partner with data engineers to build data pipelines. Partner with software engineers to integrate solutions with business platforms.
• Work closely with the business and data science lead to recommend and develop models for GI financial underwriting, medical underwriting, marketing analytics and other business use cases.
• Manage external vendors in the execution of the data science development process.

Qualifications

• Advanced degree (Masters, Ph.D.) in Mathematics, Statistics, Engineering, Econometrics, Physics, Computer Science, Actuarial, Data Science, or comparable quantitative disciplines.
• Working on complex problems in which analysis of situations or data requires an in-depth evaluation of various factors. Exercises judgment within broadly defined practices and policies in selecting methods, techniques, and evaluation criteria for obtaining results.
• Knowledge of business concepts, tools and processes that are needed for making sound decisions in the context of the company’s business.
• Experience in research and designing experiments (e.g., A/B testing). An insurance actuary background is preferred but not required.
• Ability to learn creative skills and knowledge on an on-going basis through self-initiative and solving challenges.
• Excellent problem solving, communication and collaboration skills.

Applied experience with several of the following:

• Data Acquisition and Transformation: Acquiring data from disparate data sources using APIs, SQL and NoSQL. Transform data using SQL, NoSQL, and Python. Visualize data using a diverse tool set including but not limited to Python and R.
• Database Management System: Knowledge of how databases are structured and function in order to use them efficiently — may include multiple data environments, cloud/AWS, primary and foreign key relationships, table design, database schemas, etc.
• Model Deployment: Understanding of MDLC (Model Development Life Cycle), CI/CD/CT pipelines (using tools like Jenkins, CloudBees, Harness, etc.), A/B testing. Pipeline frameworks like MLFlow, AWS SageMaker pipeline, etc., model and data versioning.
• Statistics and Computing: Calculus, Multivariable Calculus, Linear Algebra, Differential Equations, Probability, Statistics, Applied Probability, Applied Statistics, Computer Science (Programming Methodologies), and Cloud. Knowledge of statistical techniques such as descriptive, inferential, Bayesian statistics, time series analysis, etc.
• Data Wrangling: Preparing data for further analysis; redefining and mapping raw data to generate insights; processing of large datasets (structured, unstructured).
• Machine Learning: Understanding of machine learning theory, including the mathematics underlying machine learning algorithms. Expertise in the application of machine learning theory to building, training, testing, and monitoring machine learning models. Understanding and expertise in NLP (natural language processing).
• Programming Languages: Python, R, SQL, Java or Scala, Cypher.

Compensation & Benefits

The salary range for this role is from $101,500.00 to $167,300.00. Specific pricing for the role may vary within the above range based on many factors including geographic location, candidate experience, and skills.

• Market competitive base salaries, with a yearly bonus potential at every level.
• Medical, dental, vision, life insurance, disability insurance, Paid Time Off (PTO), and leave of absences, such as parental and military leave.
• 401(k) plan with company match (up to 4%).
• Company-funded pension plan.
• Wellness Programs including reimbursement for items purchased to support personal wellbeing needs.
• Work/Life Resources to help support topics such as parenting, housing, senior care, finances, pets, legal matters, education, emotional and mental health, and career development.
• Education Benefit to help finance traditional college enrollment toward obtaining an approved degree and many accredited certificate programs.
• Employee Stock Purchase Plan: Shares can be purchased at 85% of the lower of two prices (beginning or end of the purchase period), after one year of service.