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# Senior Data Scientist

- **Company:** SMITH ARNOLD PARTNERS
- **Location:** New Haven County, CT, United States
- **Experience:** Expert
- **Salary:** $140,000 - $165,000
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Application Frameworks, ARM Architecture, Continuous Integration, IBM DB2, Relational Databases, Information Retrieval, Python (Programming Language), Knowledge Management, PostgreSQL, Machine Learning, Tensorflow, Search Technologies, Software Deployment, Software Engineering, SQL Databases, Talend, Pytorch, Retrieval-Augmented Generation, Flask (Web Framework), Large Language Models, Snowflake, Multi-Agent Systems, Deep Learning, Generative AI, Agentic-AI, Fastapi, Containerization, AI Platforms, Scikit Learn, Xgboost, Integration Frameworks, Machine Learning Operations, Drift Detection, Data Pipelines, Docker
- **Published:** November 7, 2026
- **Apply:** https://www.thejobnetwork.com/job/aa45f2f0-67b3-4de6-b3fb-4b8edd676cbb/senior-data-scientist

## About the Role

* 6+ years of experience delivering advanced analytics and machine learning solutions, including production deployment.
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 * 2+ years of hands-on experience developing and delivering enterprise generative AI and LLM solutions, including RAG, agentic workflows, evaluation, and guardrails.
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 * Strong Python and SQL programming skills, with experience using machine learning frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow.
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 * Hands-on experience with Snowflake, including Snowpark and/or Cortex, and relational databases such as PostgreSQL.
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 * Experience with vector search, embeddings, knowledge retrieval, vector databases, and hybrid search strategies.
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 * Experience partnering with software engineering teams to deploy production AI services, APIs, and data pipelines, with exposure to monitoring, CI/CD, and model lifecycle management.
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 * Ability to develop robust evaluation strategies for AI and LLM solutions and connect technical performance to measurable business outcomes.
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 * Strong communication skills and the ability to explain technical concepts, business value, risks, and tradeoffs to both technical and nontechnical stakeholders.
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 * Familiarity with model governance, responsible AI frameworks, and enterprise risk management practices.
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 * Experience with legacy-to-cloud data modernization, including IBM DB2 extracts and integration platforms such as Talend.
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 * Familiarity with ML lifecycle tools such as MLflow, containerization technologies such as Docker, and API frameworks such as FastAPI or Flask.
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 * Experience developing reusable AI frameworks, production-ready solutions, and enterprise-scale machine learning capabilities.
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## Description

Are you looking for a high-impact AI and Data Science role offering the opportunity to build cutting-edge AI solutions, drive innovation across a growing organization, and see your work make a meaningful difference? Would you enjoy combining advanced machine learning, generative AI, and agentic workflows with the opportunity to work on real-world business challenges in a mission-driven environment?, n
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 * Own the end-to-end development and delivery of AI solutions, from identifying business opportunities and exploratory analysis through production deployment and performance measurement.
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 * Design and develop innovative generative AI and LLM-powered analytics capabilities, including Deep Research solutions leveraging enterprise data through Retrieval Augmented Generation (RAG).
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 * Build intelligent agentic workflows and multi-step AI orchestration using tool integration, function calling, retrieval, and responsible AI guardrails to automate business processes.
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 * Develop advanced statistical and machine learning models supporting insurance, actuarial, claims, risk management, and investment decision-making.
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 * Engineer reusable AI capabilities, including feature pipelines, embeddings, vector search, semantic retrieval, and enterprise knowledge management strategies.
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 * Establish evaluation frameworks and success metrics to measure AI quality, accuracy, safety, performance, cost, and business impact.
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 * Partner with engineering teams to productionize AI solutions, implement CI/CD processes, establish monitoring and drift detection, and support ongoing model operations.
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 * Apply responsible AI principles throughout the development lifecycle, including fairness assessments, transparency, model documentation, and audit-ready governance.
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 * Collaborate with business stakeholders, executives, data analysts, engineers, and compliance teams to translate complex business challenges into practical AI solutions.
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 * Help establish enterprise-wide AI and machine learning best practices, reusable frameworks, development standards, and governance processes.
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