Job Classification:
Technology - Data Analytics & Management

Are you interested in building capabilities that enhances the organization's 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, you will partner with Machine Learning Engineers, Data Engineers, Data Analysts and other professionals to build state-of-the-art prediction, optimization, engagement, and behavioral models. 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 applied 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.

• Responsible for the hands-on development of advanced data science solutions comprising the portfolio developed by the Principal Data Scientist and the technical requirements specified by the Senior Lead Data Scientist. Perform hands-on data analysis, model development, training, and 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 and interpretation 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.

• Advanced degree in Statistics, Mathematics, Electrical Engineering, Physics, Computer Science, or comparable quantitative disciplines
• Proficient at 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, hypothesis testing, communication and collaboration skills.

• 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.
• 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
• Natural Language Processing & Generative AI: Experience with modeling and interpreting text analysis including NLP, LLMs (Bert, etc), and Generative AI.
• Survival Analysis: Understanding of survival modeling techniques such as Cox-Proportional Hazards and applications of modern machine learning and deep learning techniques to this domain
• Causal Inference: Understanding of causal inference techniques, specifically when applied to marketing domains; experience with marketing studies is a bonus
• Graph Machine Learning: Understanding of graph machine learning (graph neural networks, GraphSAGE, etc.); knowledge of applications to fraud detection is a plus
• 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.
• 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.
• Data Wrangling: Preparing data for further analysis; Redefining and mapping raw data to generate insights; Processing of large datasets (structured, unstructured).
• 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
• Programming Languages: Python and SQL

• 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 Programsincluding up to $1,600 a year for reimbursement of 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.