Data Scientist (Mid-Senior)
- Design, train, and evaluate machine learning models from first principles, including feature engineering, model selection, and validation
- Develop and maintain production-quality Python code for data processing, training, and inference workflows
- Build natural language processing systems for document understanding, classification, extraction, and semantic search
- Design and implement Retrieval-Augmented Generation (RAG) pipelines, including embedding strategies, retrieval evaluation, and grounding techniques
- Build and maintain agentic AI systems that coordinate models, tools, and multi-step reasoning workflows
- Work with unstructured and semi-structured data at scale
- Collaborate with engineering and infrastructure teams to deploy and operate models in production
- Analyze model performance, failure modes, and data drift, and drive iterative improvement
- Strong proficiency in Python for data science and machine learning
- Experience building ML models from scratch rather than relying exclusively on pre-trained or hosted APIs
- Solid understanding of statistics, linear algebra, and core ML concepts
- Demonstrated experience with natural language processing workflows
- Hands-on experience designing and implementing RAG systems
- Experience with agentic AI patterns and relevant tooling, such as: LangChain, LlamaIndex, or similar orchestration frameworks; Vector databases and embedding pipelines; Tool-calling, planner-executor, or multi-agent coordination patterns
- Experience working with real production data and systems
- Experience with graph databases, particularly Neo4j
- Familiarity with graph-based modeling, knowledge graphs, or Graph-RAG approaches
- Experience deploying ML systems in cloud environments such as AWS or Azure
- Exposure to MLOps, containerization, or CI/CD pipelines
- Experience with document-heavy or compliance-oriented domains
- Experience with cloud such as Azure
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