> Markdown version of [/jobs/ext/1442963-data-scientist](https://www.wearedevelopers.com/jobs/ext/1442963-data-scientist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist - **Company:** Generative Ai - **Location:** Phoenix, AZ, United States (Remote available) - **Experience:** Expert - **Salary:** $228,800.0 - $249,600.0 - **Contract:** Temporary to permanent - **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, Microsoft Azure, Data Warehousing, Python (Programming Language), Azure Machine Learning, SQL Databases, Snowflake, Generative AI, Git, Kubernetes, Machine Learning Operations, Software Version Control - **Published:** July 25, 2026 - **Apply:** https://mondo.gosnaphop.com/jobs/l/login/c8336608-98ca-11ec-8029-42010a8a0008/43a149c1-87e5-11f1-8519-024201c20d84/false?applyId=f617c7f3-821a-11f1-b0c9-024201c20d84&apply=true&returnUrl=%2Fjobs%2Fl%2Frecruiting%2Fjobapplication%2Ff617c7f3-821a-11f1-b0c9-024201c20d84%2F43a149c1-87e5-11f1-8519-024201c20d84%2Ffalse%3Fstep%3D1 ## About the Role Must Haves: * Location: candidate must be based in Pacific, Mountain, or Central time zone. This is a hard requirement, not a preference. * Minimum 4 years of experience working specifically as a Data Scientist (title and scope must match, not adjacent titles like Data Analyst or ML Engineer alone) * Must currently or most recently hold a Data Scientist title (Data Scientist, Senior Data Scientist, Staff Data Scientist, Principal Data Scientist, etc.). Candidates whose current or most recent role carries a different title (Data Analyst, ML Engineer, Analytics Engineer, etc.) * Minimum 3 years of hands on MLOps experience, specifically model deployment, monitoring, and lifecycle management in production environments (not just model development or notebooks) * Direct experience with at least one MLOps tooling stack such as MLflow, Kubeflow, SageMaker Pipelines, or Azure ML Pipelines * Master's degree in a STEM field * 4 years of proficiency in SQL * 4 years of proficiency in Python * Hands on experience with AWS or Azure cloud platforms * Proficiency with Git for version control * Strong communication skills with demonstrated ability to work cross functionally with stakeholders Nice to Haves: * Experience with Snowflake for data warehousing and analytics * Hands on experience with AWS specifically, in addition to general cloud proficiency * Startup or fast paced environment mindset with comfort navigating ambiguity * Active personal use of AI tools and familiarity with the evolving AI landscape ## Description The client is seeking a Data Scientist with deep expertise in Generative AI, agentic architectures, and MLOps to design, build, and scale end to end AI solutions while embedding Responsible AI practices across the full development lifecycle. This role requires hands on MLOps maturity, not just model building, the candidate will own how models move from experimentation into production and stay reliable once they get there., * Design and deploy end to end RAG solutions and autonomous AI agents in cloud and enterprise environments * Build and scale machine learning and AI models on cloud platforms, primarily AWS or Azure * Develop and maintain MLOps pipelines to support model deployment, monitoring, versioning, and governance * Own CI/CD for ML workflows, including automated retraining, model registry management, and rollback procedures * Implement model monitoring for drift, performance degradation, and data quality issues in production * Apply statistical modeling techniques to solve complex business problems * Collaborate with stakeholders across the organization to translate requirements into scalable AI solutions * Embed Responsible AI practices across model development, deployment, and governance workflows * Contribute across the full development lifecycle, from experimentation through production release ## Related Videos - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [Understanding Kubernetes in a visual way](https://www.wearedevelopers.com/videos/100085-understanding-kubernetes-in-a-visual-way) - [Developer Experience, Platform Engineering and AI powered Apps](https://www.wearedevelopers.com/videos/990-developer-experience-platform-engineering-and-ai-powered-apps) - [Blueprints for Success: Steering a Global Data & AI Architecture](https://www.wearedevelopers.com/videos/1577-blueprints-for-success-steering-a-global-data-ai-architecture) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)