Job Specifications
Title: Data Scientist
Location: Newark, NJ Hybrid Locals only
Duration: 12 Months with extension
Job Description
As a Data Scientist on/in the US Businesses Data Science Team you will partner with Machine Learning Engineers, Data Engineers, Business Leaders and other professionals to build GenAI and ML models to improve advisor experience, perform lead scoring, and increase sales revenue.
You will implement AI and machine learning models that will deliver stability, scalability and integration with other advisor 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.
The current work arrangement for this position is Hybrid (Newark, NJ) and requires your on-site presence on a reoccurring weekly basis as determined by your business.
Your manager will provide additional details relative to the specific number of days you are expected to be on-site.
Roles & Responsibilities: Here is what you can expect in a typical day:
Provide deep technical leadership to a portfolio of high impact data science initiatives involving sales and advisor experience.
Identify the optimal sets of data, models, training, and testing techniques required for successful product delivery. Remove complex technical impediments
Leverage your experience and skills to identify new opportunities where data science and AI can improve experiences, gain efficiencies, and generate sales.
Manage team members in AI/ML and model development, testing, training, and tuning. Apply hands-on experience to ensuring best-in-class model development. Mentor team members in technical skill development and product ownership.
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 as needed.
Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code on Prudential’s AI/ML platform.
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.
Familiarity with Python, SQL, AWS, and JIRA.
Familiarity with LLMs, deployment of LLMs, RAG, LangChain, LangGraph, and Agentic AI concepts.
The Skills and expertise you bring:
Applied Statistics, Computer Science, or Engineering or experience in related fields with a focus on machine learning, AI, and LLMs.
Junior category industry experience with responsibility for developing and delivering advanced quantitative, AI/ML, analytical and statistical solutions.
Ability to lead a small team with minimal guidance and effectively leverage diverse ideas, experiences, thoughts and perspectives to the benefit of the organization to deliver AI products.
Ability to influence business stakeholders and to drive adoption of AI/ML solutions.
Experience with agile development methodologies, Test-Driven Development (TDD), and product management.
Knowledge of business concepts, tools and processes that are needed for making sound decisions in the context of the company's business
Demonstrated ability to mentor and operational management of data science team based on project requirements, resourcing requirements, and planning dependencies as appropriate, anticipate risks and bottlenecks and proactively takes actions
Excellent problem solving, communication and collaboration skills, and stakeholder management
Significant experience and/or deep expertise with several of the following:
Machine Learning and AI: 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.
Expertise in traditional machine learning models (unsupervised, XGBoost, etc.) and Large Language Models (OpenAI, Claude).
Model Deployment: Understanding of model development life cycle, CI/CD/CT pipelines (using tools like Jenkins, CloudBees, Harness, etc.), A/B testing, and pipeline frameworks such as AWS SageMaker, and newer AWS/Azure Agentic AI infrastructure products.
Data Acquisition and Transformation: Acquiring data from disparate data sources using APIs and SQL. T