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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist - Applied AI - **Company:** NTT DATA, Inc. - **Location:** United States (Remote available) - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Unity 3d, Artificial Intelligence, Amazon Web Services, Data Analysis, ARM Architecture, Microsoft Azure, Information Systems, Continuous Integration, Data Cleansing, Information Engineering, Data Infrastructure, Information Leak Prevention, Extract Transform Load (ETL), Database Queries, DevOps, Python (Programming Language), Metadata, Meta-Data Management, Role-Based Access Control, Power BI, DataOps, Search Technologies, SQL Databases, Enterprise Data Management, Cloud Platform System, Data Classification, Delivery Pipeline, Snowflake, Generative AI, Microsoft Fabric, Pyspark, Semi-structured Data, Information Technology, Data Lineage, Machine Learning Operations, Software Version Control, Databricks - **Published:** July 22, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/p6l9xo2ki0 ## About the Role * 3-5 years of experience in a data-focused role, such as Data Engineer, Analytics Engineer, Data Analyst, BI Developer, Data Platform Consultant or Applied AI Consultant. * Hands-on experience with at least one modern data platform, such as Snowflake, Databricks, Microsoft Fabric, Azure, AWS or GCP, with willingness to develop capability across multiple platform ecosystems. * Strong SQL skills and practical experience working with structured and semi-structured data. * Good understanding of data engineering or analytics engineering concepts, including data modelling, pipelines, ELT/ETL, data quality and analytical data products. * Experience using Python, PySpark, notebooks or equivalent platform tooling for data preparation, analysis, testing or automation. * Understanding of semantic models, business definitions, metrics, metadata and how they improve the reliability of AI-enabled analytics. * Awareness of Generative AI concepts, including prompts, embeddings, vector search, semantic search, RAG, AI agents and natural language analytics. * Ability to configure, test and validate platform AI capabilities using real data, metadata, semantic models and business scenarios, rather than only discussing AI concepts at a high level. * Understanding of enterprise delivery considerations such as security, access control, privacy, governance, documentation, cost management and operational handover. * Clear communication skills and ability to work in mixed teams across data engineering, architecture, analytics and business stakeholders. Nice to have: * Experience with Snowflake AI capabilities such as Cortex AI, Cortex Analyst, Cortex Search, Cortex Agents, Snowpark or Snowflake-native governance features. * Experience with Databricks AI capabilities such as Mosaic AI, Genie, AI/BI, Vector Search, MLflow, Unity Catalog, Model Serving or agent-related features. * Experience with Microsoft Fabric capabilities such as Copilot in Fabric, Data Factory, Data Engineering, Data Science, Power BI semantic models, OneLake, Direct Lake, Fabric Data Agents or Fabric governance capabilities. * Experience implementing AI-enabled analytics, conversational data products, semantic search or enterprise knowledge retrieval solutions. * MSc or equivalent professional experience in Data, Computer Science, Information Systems, Engineering, Mathematics, Analytics, Business Intelligence or a related discipline. * Experience with metadata management, semantic modelling, data catalogues, business glossaries, metric layers or BI semantic layers. * Exposure to enterprise architecture, solution design, platform governance or cloud data platform implementation. * Experience with DevOps or DataOps practices, including version control, CI/CD, deployment pipelines and environment management. * Experience working in consulting, client delivery or internal platform enablement roles. ## Description * Support use cases such as natural language querying, AI-assisted analytics, document and data search, data classification, automated insight generation and conversational access to enterprise data. * Build and configure platform-native AI features such as Cortex Analyst, Cortex Search, Cortex Agents, Databricks AI/BI, Genie, Mosaic AI capabilities, Microsoft Fabric Copilot and Fabric Data Agent-style solutions where appropriate. * Ground AI outputs in governed data sources, semantic models, metadata, business definitions and reusable data products. * Support implementation of Retrieval Augmented Generation (RAG), vector search, semantic search and enterprise knowledge retrieval where aligned to client needs. * Evaluate AI-generated outputs for accuracy, grounding, relevance, hallucination risk, data leakage and business usability. Data engineering, analytics and platform delivery * Prepare and structure data so that it can be used effectively by AI-enabled analytics and data platform capabilities. * Work with SQL, Python, PySpark, notebooks or platform-native tooling to transform, test and expose data for downstream AI and analytics use cases. * Collaborate with data engineers and architects to ensure AI solutions are built on reliable, secure and well-governed data foundations. * Support implementation of pipelines, notebooks, semantic layers, governed datasets and analytical data products. * Help define reusable implementation patterns for AI-enabled data products across Snowflake, Databricks and Microsoft Fabric. * Contribute to documentation, technical designs, test plans, operational handover materials and reusable delivery playbooks. Enterprise readiness and governance * Help make AI-enabled solutions enterprise-ready by considering access control, security, privacy, data lineage, observability, cost management and operational support. * Work with platform governance features such as role-based access control, catalogues, sensitivity labels, lineage, model or agent governance and auditability. * Support responsible AI practices, including transparency, explainability, human oversight and appropriate control of AI-generated outputs. * Test AI-enabled outputs for quality, reliability, business relevance, potential misuse and maintainability. * Ensure solutions are designed for controlled adoption and supportability, not just demo value. Client and stakeholder engagement * Work with client stakeholders to understand business problems and identify where platform-native AI capabilities can help. * Translate business requirements into practical AI-enabled data platform solutions, delivery patterns and measurable outcomes. * Explain technical concepts clearly to both technical and non-technical audiences. * Support demos, proof-of-concepts, workshops, internal enablement sessions and client adoption activities. * Contribute to reusable assets, accelerators, templates and capability-building material for the Data Practice., We offer a range of tailored benefits that support your physical, emotional, and financial wellbeing. Our Learning and Development team ensure that there are continuous growth and development opportunities for our people. 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