Pay Rate Range: $ 60.60 - 62.87/hr.
Job Description:
Data Scientist with Generative AI|Agentic AI|Natural Language Processing (NLP)|Large Language Models (LLMs)|Data Science|Machine Learning (ML)
Must Have Technical/Functional Skills
• 7+ years of relevant experience in data science, analytics, machine learning, or AI.
• Expertise in statistical modeling, machine learning, generative AI, agentic AI, and natural language processing.
• Strong knowledge of LLMs, prompt engineering, RAG, autonomous decision-making frameworks, and human- in-the-loop workflows.
• Programming expertise in Python, R, SQL, SAS, or similar analytical tools, with cloud-based or distributed platforms such as Databricks.
• Experience with vector databases, semantic search, knowledge graphs, metadata management, enterprise search, and production AI deployment and monitoring.
Roles & Responsibilities
Design and implement advanced machine learning, generative AI, and agentic AI solutions across structured and unstructured data.
· Build and deploy scalable, automated data science products and reusable analytical assets.
· Design agentic AI architectures using LLMs, retrieval systems, structured data, APIs, tools, business rules, routing, and approval checkpoints.
· Optimize RAG solutions and develop evaluation frameworks covering accuracy, reliability, hallucination risk, latency, cost, safety, and consistency.
· Partner with engineering, platform, business, stakeholders, and clients to deploy, monitor, maintain, and communicate production-ready AI solutions.
Generic Managerial Skills, If any
• Lead complex analytics initiatives and provide technical guidance.
• Mentor data scientists and lead code reviews.
• Make analytical and architectural decisions and establish technical standards.
• Communicate complex analytical findings to technical and non-technical audiences.
• Collaborate with stakeholders and clients across strategic, operational, and clinical objectives.
Key Words to search in Resume
• AI Engineer; Data Scientist; machine learning; statistical modeling; natural language processing.
• Generative AI; Agentic AI; LLM; prompt engineering; RAG; autonomous decision-making; human-in-the-loop.
• Python; R; SQL; SAS; Databricks; distributed computing; cloud data platforms.
• Vector database; semantic search; knowledge graph; metadata management; enterprise search.
• Responsible AI; model governance; explainability; bias detection; hallucination evaluation; production AI monitoring.
Pre-Screening Questionnaire
• Do you have 7 or more years of relevant experience in data science, analytics, machine learning, or AI?
• Describe your hands-on experience with machine learning, generative AI, agentic AI, NLP, LLMs, prompt engineering, and RAG.
• Have you designed agentic workflows using retrieval systems, APIs, tools, business rules, routing logic, escalations, and human approval checkpoints?
• Which programming languages and platforms have you used, including Python, R, SQL, SAS, Databricks, cloud, or distributed computing platforms?
• Have you deployed and monitored production AI solutions and applied responsible AI, model governance, explainability, bias detection, validation, and risk controls?
Skills: Digital : Data Science~Digital : Natural Language Processing (NLP)~AI & Gen AI - Products & Tools
Experience Required: 6-8 years
| Skills: | | Category | Name | Required | Importance | Experience |
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| SkillCategoryTest1_MN | AI & Gen AI - Products & Tools | Yes | 1 | >7 years | | | SkillCategoryTest1_MN | Digital : Data Science | Yes | 1 | >7 years | | | SkillCategoryTest1_MN | Digital : Natural Language Processing(NLP) | Yes | 1 | >7 years | |
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