[Remote] Data Scientist - remote

Note: The job is a remote job and is open to candidates in USA. JSR Tech Consulting is a leading financial services institution looking for a Data Scientist to join their Global Technology team. The role involves designing and developing production-grade AI capabilities to support Advisors in the U.S. Businesses, requiring a blend of AI engineering expertise and business acumen.

Responsibilities

• 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

Skills

• Advanced degree (Masters, Ph.D.) 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
• 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
• 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
• 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
• 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
• 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
• Preparing data for further analysis; Redefining and mapping raw data to generate insights; Processing of large datasets (structured, unstructured)
• 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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