Data Science Lead

Right to hire position with a major financial firm.

hybrid, in Newark, NJ

As a Lead, Data Scientist you will partner with Machine Learning Engineers, Data Engineers, Data Analysts and other professionals to build AI & Machine Learning products. You will implement Machine Learning, AI, and agentic capabilities that will deliver stability, 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 advanced 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.

• Provide deep technical leadership to a portfolio of high impact data science initiatives. Identify the optimal sets of data, models, training, and testing techniques required for successful product delivery. Remove technical impediments.
• Manage team members in data analysis, model development (traditional machine learning & statistical models) GenAI & agent development, testing, training, and tuning. Apply hands-on experience to ensuring best-in-class model development. Mentor team members in technical skill development.
• Write production grade code and partner with machine learning engineers to push model code into production including traditional machine learning, statistical models, GenAI and agentic solutions.
• Experience with engineering of Agentic AI systems, fine-tuning technique (ex: LoRA), deployment of LLMs, RAG, Agentic RAG, Strands, Claude Agents SDK, Agents SDK and Agentic AI concepts.
• 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.
• Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
• Bring a strong understanding of relevant and emerging technologies, provide input and coach team members and embed learning and innovation in the day-to-day
• Use programming languages including but not limited to Python, SQL.

• Advanced degree (Masters, Ph.D.) in Mathematics, Statistics, Engineering, Econometrics, Physics, Computer Science, Actuarial, Data Science, or comparable quantitative disciplines.
• 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.
• 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.
• Ability to influence business stakeholders and to drive adoption of AI/ML solutions.
• Experience with agile development methodologies and Test-Driven Development (TDD).
• Knowledge of business concepts, tools and processes that are needed for making sound decisions in the context of the company's business.
• Ability to learn new skills and knowledge on an on-going basis through self-initiative and tackling challenges.
• Excellent problem solving, communication and collaboration skills.
• Experience in using Cloud based AI Platforms like Bedrock and Sagemaker AI.

• Data Acquisition and Transformation :
  Acquiring data from disparate data sources using API's, semantic data models, 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, SQL (relational), Unstructured (No
  
  SQL), Graph/ontology (Graph DB), semantic data models.
• Data Analysis and Insights :
  Analyzing structured and unstructured data using data visualization, manipulation, and statistical methods to identify patterns, anomalies, relationships, and trends.
• Statistics and Computing :
  Exceptional understanding of:
  Multi-variable 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 :
  Deep understanding of machine learning theory, including the mathematics underlying machine learning algorithms.…