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JSR Tech Consulting · 1 day ago

Data Scientist - Director Level / Lead

New Jersey, United States

Full-time

Hybrid

Lead/Staff, Director/Executive

$80/hr - $100/hr

8+ years exp

JSR Tech Consulting is a major investment firm seeking a Director Level Data Scientist. In this high visibility role, you will lead a team of data scientists to develop AI tools, work closely with various stakeholders, and oversee model deployment to enhance business operations and advisor experiences.

Information ServicesInformation Technology

Responsibilities

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 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

Qualification

Machine LearningAIModel DeploymentData AcquisitionPythonSQLAgile DevelopmentCommunication SkillsProblem SolvingTeam Leadership

Required

Masters or Ph.D. in Applied Statistics, Computer Science, or Engineering or experience in related fields with a focus on machine learning, AI, and LLMs

8-10 years of relevant 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

Familiarity with Python, SQL, AWS, and JIRA

Familiarity with LLMs, deployment of LLMs, RAG, LangChain, LangGraph, and Agentic AI concepts

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. 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