Junior Data Scientist

Job Title: Junior Data Scientist

Location: Newark, NJ (Hybrid)

Duration: 12 Month Contract to Hire

Interview: Video

Key Responsibilities:

• Design and develop production-grade Generative AI and agentic solutions aligned to technical requirements and the Data Science Lead’s portfolio.

• Perform hands-on context engineering, agent design, model integration, and end-to-end AI system development.

• Design and build AI agent harnesses, orchestration frameworks, and context engineering pipelines.

• Develop and integrate Model Context Protocol (MCP) servers to securely expose enterprise tools, data sources, and APIs to AI agents.

• Implement Agent-to-Agent (A2A) communication patterns and multi-agent architectures to solve complex, multi-step business problems.

• Write production-level code and support AI solutions through the full lifecycle from development to deployment.

• Partner with Machine Learning Engineers to productionize AI solutions.

• Partner with Data Engineers to build and maintain data pipelines.

• Partner with Software Engineers to integrate AI solutions into business platforms.

• Continuously research and apply emerging algorithms, agentic frameworks, and AI application patterns.

Must-Have Qualifications:

• Advanced degree (Master’s or Ph.D.) in Mathematics, Statistics, Engineering, Econometrics, Physics, Computer Science, Actuarial Science, Data Science, or related quantitative discipline.

• Experience solving complex problems requiring in-depth evaluation of multiple factors and sound judgment within broadly defined practices.

• Demonstrated ability to learn new skills and technologies through self-initiative.

• Strong problem-solving, communication, and collaboration skills.

Preferred Experience:

AI Engineering & Production AI Lifecycle:

• Experience designing, building, and delivering AI systems end-to-end in production environments

• Knowledge of CI/CD for AI, model versioning, observability, monitoring, and responsible AI practices

Generative AI, Agentic Systems & Context Engineering:

• Hands-on experience with LLMs, RAG, LangChain, LangGraph, vector databases

• Expertise in prompt engineering, dynamic context construction, structured outputs

• Experience with agent harnesses, orchestration frameworks, MCP servers, A2A / multi-agent architectures

Machine Learning:

• Strong understanding of machine learning theory and its application to training, testing, interpreting, and monitoring models

Data Acquisition & Transformation:

• Experience acquiring data via APIs and SQL

• Data transformation using Python and SQL

• Data visualization using Python-based tools

Data Management & Wrangling:

• Experience working with large structured and unstructured datasets

• Knowledge of database design, schemas, and relationships

AWS / DevOps:

• Familiarity with AWS environments including development, QA, staging, and production deployments

Programming Languages:

• Python, SQL