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 Senior Data Scientist supporting Retirement Strategies in the U.S. Businesses (USB) Service, Data and 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 is an exciting opportunity to be a part of a strategic initiative that is evolving and growing over time! In addition to applied experience, you will bring excellent problem solving, communication and teamwork skills, along with agile ways of working, strong business insight, an inclusive leadership demeanor and a continuous learning focus to all that you do.

This role is based in our office in Newark, NJ. 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 your on-site presence on a reoccurring weekly basis at least 3 days per week.

Here is what you can expect in a typical day:

• Responsible for the hands-on development of advanced data science solutions comprising the portfolio developed by the Lead Data Scientist and the technical requirements specified by the Lead Data Scientist. Perform hands-on data analysis, model development, model training, model testing.
• Write production-level code and partner with machine learning engineers to push development code into production.
• Continuously research new methods for problem solution, including new algorithms, modeling techniques, and data analytics techniques.
• Partner with machine learning engineers to productionized machine learning models. Partner with data engineers to build data pipelines. Partner with software engineers to integrate solutions with business platforms.

The Skills and expertise you bring:

• 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.
• Ability to learn new skills and knowledge on an ongoing basis through self-initiative and seeking challenges
• Excellent problem solving, communication and collaboration skills

Applied experience with several of the following:

• 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, interpreting and monitoring machine learning models
• Generative AI & Natural Language Processing: Experience with modeling and interpreting text analysis including NLP, LLMs (BERT, etc), and Generative AI. Experience in modern Gen AI technologies including RAG, LangChain, LangGraph, vector DB and their application in Retirement Strategies sales enablement area. 
• Statistics and Computing: Exceptional understanding of: Multivariable Calculus, Linear Algebra, Differential Equations, Applied Probability, Applied Statistics, Computer Science (Programming Methodologies), and Cloud. Knowledge of statistical techniques such as the use of descriptive, inferential, Bayesian statistics, time series analysis etc. to extract business insights and experimentation to solve business problems.
• Data Acquisition and Transformation: Acquiring data from disparate data sources using API's and SQL. Transform data using SQL and Python. Visualizing data using a diverse tool set including but not limited to Python.