Job Title: Data Scientist Location: Remote Duration: 12+ Months (Temp to Hire) Here is what you can expect in a typical day: * Responsible for the hands-on design and development of production-grade GenAI and Agentic solutions comprising the portfolio developed by the Data Science Lead and the technical requirements specified. 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 expose tools, data sources, and enterprise APIs to AI agents in a standardized, secure manner; and implement Agent-to-Agent (A2A) communication patterns and multi-agent architectures to solve complex, multi-step business problems. * Write production-level code and partner with machine learning engineers and platform teams to deliver AI solutions from development through production following the full AI lifecycle. * Continuously research new methods for problem solution, including new algorithms, agentic frameworks, context management techniques, and AI application patterns. * Partner with machine learning engineers to productionize AI solutions. Partner with data engineers to build data pipelines. Partner with software engineers to integrate solutions with business platforms. The Skills and expertise you bring: * Advanced degree in Mathematics, Statistics, Engineering, Econometrics, Physics, Computer Science, Actuarial, Data Science, or comparable quantitative disciplines * Working on complex problems in which analysis of situations or data requires an in-depth evaluation of various factors. Exercises judgment within broadly defined practices and policies in selecting methods, techniques and evaluation criteria for obtaining results. * Ability to learn new skills and knowledge on an ongoing basis through self-initiative and seeking challenges * Excellent problem solving, communication and collaboration skills Applied experience with several of the following: * AI Engineering & Production AI Lifecycle: Ability to design, build, and deliver AI systems end-to-end in a production environment. Deep understanding of the AI lifecycle - from problem framing and data preparation through model development, evaluation, deployment, monitoring, and continuous improvement. Experience with CI/CD for AI, model versioning, observability, and responsible AI practices. * Generative AI, Agentic & Context Engineering: Expertise in modern Generative AI and NLP technologies including LLMs, RAG, LangChain, LangGraph, vector databases, etc. Skilled in context engineering - prompt engineering, dynamic context construction, context window management, and structured output design. Experience building AI agent harnesses and orchestration frameworks including scaffolding, tool registries, and evaluation loops. Hands-on experience designing MCP servers to expose enterprise tools and APIs to AI agents, and implementing Agent-to-Agent (A2A) communication patterns and multi-agent architectures to solve complex, multi-step business problems. * Machine Learning: 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 * Data Acquisition and Transformation: Acquiring data from disparate data sources using API's 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, etc. * Data Wrangling: Preparing data for further analysis; Redefining and mapping raw data to generate insights; Processing of large datasets (structured, unstructured). * AWS DevOps: Experience in the project development life cycle in an AWS environment. Familiar with development, QA, staging and production deployment stages. * Programming Languages: Python, SQL
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