Note: The job is a remote job and is open to candidates in USA. Nesco Resource is seeking a Senior Applied AI & Data Scientist for a permanent remote opportunity. The role involves designing, building, evaluating, and deploying AI-powered solutions to achieve measurable business outcomes, while collaborating with various stakeholders to translate complex questions into production-ready capabilities.

Responsibilities

• Own end-to-end delivery of AI solutions from problem framing and exploratory analysis through production deployment and measurement
• Design and deliver LLM-enabled analytics and Deep Research capabilities using RAG over structured and unstructured enterprise data
• Build agentic workflows and multi-step orchestration (tool use, function calling, retrieval, and guardrails) to automate business processes
• Develop and deploy advanced statistical and machine learning models supporting insurance, actuarial, claims, risk, and investment decision-making
• Engineer features and context: build reusable feature pipelines, embeddings, vector search patterns, and semantic/metadata strategies
• Define success criteria and evaluation plans: offline tests, human-in-the-loop review, and online measurement (A/B or phased rollout)
• Productionize and operate models: partner with engineers to implement CI/CD, monitoring, drift detection, prompt/version management, and incident response runbooks
• Apply responsible AI practices consistently: bias and fairness assessment, transparency, documentation (model cards), and audit-ready controls
• Communicate insights and tradeoffs clearly to executives and technical teams (risk, compliance, security) and influence decisions with data
• Contribute to reusable standards and patterns for MLOps/LLMOps across the enterprise (templates, libraries, and governance checklists)

Skills

• Strong Python and SQL skills; experience with ML libraries (e.g., scikit-learn, XGBoost, PyTorch/TensorFlow) and data science best practices
• Hands-on experience with Snowflake (Snowpark and/or Cortex) and relational databases such as PostgreSQL
• Experience with vector search/embeddings and knowledge retrieval patterns; familiarity with vector databases and hybrid search
• Experience partnering with engineering on production services (APIs, batch/stream pipelines), monitoring, and CI/CD
• Ability to define and run robust evaluation for models/LLMs (quality, safety, performance, cost) and translate into business KPIs
• 6+ years delivering advanced analytics and machine learning solutions, including production deployment
• 2+ years delivering GenAI/LLM solutions (RAG, agents, evaluation/guardrails) in an enterprise environment
• Financial services experience (life insurance, annuities, investments preferred) and familiarity with regulated model governance
• Experience with legacy-to-cloud data modernization (e.g., IBM DB2 extracts) and integration tools (e.g., Talend) where relevant
• Experience with ML lifecycle tooling (e.g., MLflow or equivalent), containerization (Docker), and API frameworks (FastAPI/Flask)

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