Note: The job is a remote job and is open to candidates in USA. Cloudera is a company that empowers organizations to transform complex data into actionable outcomes. They are seeking a talented Senior Data Scientist to design and deliver AI-powered systems that enhance decision-making and operational excellence, utilizing strong statistical foundations and advanced programming expertise.
Responsibilities
- Design, develop, and deploy GenAI-powered internal applications, copilots, and workflow accelerators
- Build reusable AI components, including retrieval pipelines, structured prompting patterns, orchestration workflows, and evaluation harnesses
- Design retrieval strategies that connect LLMs to trusted internal knowledge sources, ensuring grounded and reliable outputs
- Develop and maintain statistical and machine learning models to support automation, optimization, forecasting, and classification use cases
- Implement evaluation and validation frameworks to measure quality, accuracy, and consistency of AI-driven systems
- Partner cross-functionally to identify high-value opportunities for AI enablement across the organization
- Create reusable datasets, feature pipelines, and experimentation frameworks to support iterative development
- Uphold high standards for quality, reliability, and responsible AI practices
- Contribute to peer review processes to ensure technical rigor and maintainability
- Document methodologies, assumptions, and implementation details to ensure transparency and reproducibility
Skills
- Hands-on experience building applications or workflows powered by large language models (LLMs)
- Evidence of a builder mindset through shipped AI tools, internal platforms, or automation solutions
- Demonstrated experience applying machine learning techniques in production or enterprise environments
- 5+ years of relevant experience in Data Science, Machine Learning, or AI-focused roles
- Strong curiosity for emerging AI technologies and the ability to evaluate and adopt them responsibly
- Academic background in a quantitative discipline such as Statistics, Mathematics, Computer Science, Engineering, Economics, or a related field
- Generative AI & LLM Engineering: Hands-on experience working with large language models (LLMs) and modern AI tooling. This includes prompt design, structured output generation, retrieval-augmented generation (RAG), evaluation strategies, and workflow automation. Ability to translate GenAI capabilities into reliable, enterprise-ready solutions that integrate with existing systems and data sources
- AI Application Development Experience: rapidly prototyping and iterating on internal applications, copilots, or AI-enabled workflow tools. Comfortable evolving prototypes into maintainable, production-grade solutions. Familiarity with modern development frameworks (e.g., Streamlit, Gradio, FastAPI, or similar) is beneficial
- Platform-Oriented Thinking: Demonstrated ability to design reusable components such as shared prompt libraries, retrieval pipelines, evaluation frameworks, and standardized integration patterns that enable scalable AI adoption
- Data Science & Machine Learning Expertise: Proficiency in Python (or R) for data preparation, feature engineering, statistical modeling, and machine learning. Experience with core data science libraries (e.g., Pandas, NumPy, scikit-learn) and a solid understanding of supervised and unsupervised learning methods
- Strong Mathematical and Statistical Foundation: Deep understanding of probability, statistical inference, experimentation, and quantitative reasoning to ensure model robustness and reliability
- SQL & Data Fluency: Strong understanding of relational databases and the ability to quickly learn new schemas and data environments. Comfortable writing efficient, production-grade SQL to support modeling, experimentation, and AI-enabled applications
- Exceptional Communication Skills: Ability to translate complex business challenges into technical solutions and clearly communicate findings, trade-offs, and recommendations to both technical and non-technical stakeholders
- Collaborative Development Experience: Experience working in collaborative environments such as Cloudera Data Science Workbench, Jupyter, Zeppelin, or similar platforms
- GitHub Proficiency: Experience using version control to support collaboration, code review, documentation, and long-term maintainability
- Experience with vector databases, embedding models, or semantic retrieval systems
- Experience designing internal AI platforms or shared enablement frameworks
- Familiarity with API-driven architectures and integrating AI capabilities into enterprise systems
- Exposure to responsible AI practices, governance frameworks, or model lifecycle management
Benefits
- Generous PTO Policy
- Support work life balance with Unplugged Days
- Flexible WFH Policy
- Mental & Physical Wellness programs
- Phone and Internet Reimbursement program
- Access to Continued Career Development
- Comprehensive Benefits and Competitive Packages
- Paid Volunteer Time
- Employee Resource Groups
Company Overview