Data Scientist, Newark, NJ (Hybrid) - Full-time

Hiring: Data Scientist

Location: Newark, NJ (Hybrid) W2 Only

 

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 continuous learning focus on all that you do.

Here is what you can expect on 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 experience, gain efficiency, and generate sales.

• Apply hands-on experience to ensure best-in-class model development.

• Communicate clearly and concisely, in writing and verbally, all facets of model design and development. Continuously look for insights into developed models and generate new ideas for model improvement.

• Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code on AI/ML platform.

• Bring a deep understanding of relevant and emerging technologies 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.

 

• 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

• Demonstrate ability to mentor and operate management of data science team based on project requirements, resourcing requirements, and planning dependencies as appropriate, anticipating risks and bottlenecks and proactively taking actions

• Excellent problem solving, communication and collaboration skills, and stakeholder management

Experience and/or deep expertise with several of the following:

• Machine Learning and AI: Understanding 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. Transform data using SQL and Python. Visualizing data using a diverse tool set including but not limited to Python.

• Database Management Systems: Knowledge of how databases are structured and function in order use them efficiently. May include multiple data environments, cloud/AWS, primary and foreign key relationships, table design, database schemas, etc.

• Data Analysis and Insights: Analyzing structured and unstructured data using data visualization, manipulation, and statistical methods to identify patterns, anomalies, relationships, and trends.

• Programming Languages: Python and SQL.