Data Tech Lead

About the role

As a Lead Data Scientist, you will partner with Machine Learning Engineers, Data Engineers, Data Analysts, and other professionals to build AI and Machine Learning products. You will implement Machine Learning, AI, and agentic capabilities to deliver stability, scalability, and integration with other products and services, solving sophisticated business problems and deploying innovative solutions.

Responsibilities

• Provide deep technical leadership for a portfolio of high-impact data science initiatives.
• Identify optimal data sets, models, training, and testing techniques for successful product delivery.
• Remove technical impediments and manage team members in data analysis, model development (traditional ML, statistical models, GenAI, and agent development), testing, training, and tuning.
• Apply hands-on experience to ensure best-in-class model development and mentor team members in technical skill development.
• Write production-grade code and partner with machine learning engineers to deploy models, including traditional ML, statistical models, GenAI, and agentic solutions.
• Demonstrate experience with engineering Agentic AI systems, fine-tuning techniques (e.g., LoRA), deployment of LLMs, RAG, Agentic RAG, Strands, Claude Agents SDK, and Agentic AI concepts.
• Communicate clearly and concisely, in writing and verbally, all facets of model design and development.
• Continuously analyze models for insights and generate new ideas for improvement.
• Leverage CI/CD best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
• Bring a strong understanding of emerging technologies, coach team members, and embed learning and innovation in daily work.
• Use programming languages including Python and SQL.

Requirements

• Advanced degree (Masters, Ph.D.) in Mathematics, Statistics, Engineering, Econometrics, Physics, Computer Science, Actuarial Science, Data Science, or comparable quantitative discipline.
• Ability to lead a small team with minimal guidance and leverage diverse perspectives to deliver AI products.
• Demonstrated ability to mentor and manage a data science team, anticipate risks, and proactively address bottlenecks.
• Ability to influence business stakeholders and drive adoption of AI/ML solutions.
• Experience with agile development methodologies and Test-Driven Development (TDD).
• Knowledge of business concepts, tools, and processes for sound decision-making.
• Commitment to continuous learning and skill development through self-initiative.
• Excellent problem-solving, communication, and collaboration skills.
• Experience with Cloud-based AI platforms like Bedrock and SageMaker AI.

Skills

• Data Acquisition and Transformation: Acquiring data from disparate sources using APIs, semantic data models, and SQL; transforming data using SQL and Python; visualizing data using tools like Python.
• Database Management System: Knowledge of database structures and functions, including cloud/AWS environments, relational (SQL), NoSQL, Graph/ontology (Graph DB), and semantic data models.
• Data Analysis and Insights: Analyzing structured and unstructured data using visualization, manipulation, and statistical methods to identify patterns, anomalies, and trends.
• Statistics and Computing: Exceptional understanding of multivariable calculus, linear algebra, differential equations, applied probability, applied statistics, computer science (programming methodologies), and cloud computing. Knowledge of statistical techniques such as descriptive, inferential, Bayesian statistics, time series analysis, and experimentation.
• Machine Learning: Deep understanding of machine learning theory, including the mathematics underlying algorithms. Expertise in building, training, testing, and monitoring supervised (regression, classification) and unsupervised (clustering, anomaly detection) models.
• Generative AI & Natural Language Processing: Experience with text analysis, NLP, LLMs, and Generative AI. Proficiency in modern Gen AI technologies including RAG, LangChain, LangGraph, vector DBs, LangFuse, AgentCore, and Agents, particularly in Retirement Strategies.
• Model Deployment: Understanding of the model development lifecycle, A/B testing, CI/CD pipelines, and frameworks such as AWS SageMaker and newer AWS/Azure Agentic AI infrastructure products.
• Programming Languages: Python, SQL.

This is a hybrid role based in Newark, NJ.