JSR Tech Consulting · 18 hours ago
Data Scientist
JSR Tech Consulting is seeking a Data Scientist to join their Data Science Team. In this role, you will collaborate with various stakeholders to design and implement GenAI and machine learning models that enhance advisor experiences and support revenue growth.
Responsibilities
Provide technical leadership on high-impact data science initiatives focused on sales enablement and advisor experience
Design, train, and evaluate ML/AI models, including lead scoring and GenAI solutions
Identify new business opportunities through AI, proposing novel use cases and solutions
Manage and mentor team members in AI/ML techniques, model development, testing, and deployment
Communicate model concepts and findings clearly and effectively, both verbally and in writing
Oversee vendor contributions when necessary in support of model development
Implement CI/CD best practices to support model deployment on the company’s AI/ML platform
Stay current on emerging AI technologies and embed innovation into daily practice
Work on complex and unique problems requiring evaluation of abstract variables
Utilize tools and languages such as Python, SQL, AWS, and JIRA
Work with modern GenAI tools including LLMs, RAG, LangChain, LangGraph, and Agentic AI concepts
Qualification
Required
Background in Applied Statistics, Computer Science, Engineering, or a related discipline
Industry experience developing and delivering advanced AI/ML and statistical solutions
Ability to lead small teams independently and build diverse, high-performing environments
Experience influencing stakeholders and driving AI/ML adoption across functions
Agile development and product management experience, including Test-Driven Development (TDD)
Sound business acumen with knowledge of decision-making processes and operational strategy
Proven experience managing and mentoring data science teams
Strong problem solving, communication, collaboration, and stakeholder engagement skills
Deep understanding of machine learning theory and its practical application
Expertise in building, training, testing, and monitoring ML models
Familiarity with traditional ML techniques (e.g., unsupervised learning, XGBoost) and LLMs (e.g., OpenAI, Claude)
CI/CD/CT pipeline experience (e.g., Jenkins, CloudBees, Harness)
Exposure to tools like AWS SageMaker and Azure AI agentic infrastructure
A/B testing frameworks and model lifecycle management
Skilled in data acquisition using APIs and SQL
Data transformation and visualization using Python and SQL
Ability to analyze structured and unstructured data for insights and trends
Understanding of relational database structures, schemas, and key relationships
Experience working across multiple environments, including cloud (AWS preferred)