Senior Applied AI & Data Scientist(Contract to hire)

Iris Software Inc.; United States; Remote; USD 180,000 - 260,000; Part time

Job summary

Iris Software Inc. is seeking a Senior AI & Data Scientist to design, build, and deploy AI-powered solutions across Life Insurance, Investments, Membership, and Charities. This remote, contract role emphasizes end-to-end delivery, governance, and measurable business impact.

You will leverage Snowflake, PostgreSQL, and RAG-based approaches with responsible AI controls in an enterprise-scale data ecosystem, collaborating with engineering for production services and CI/CD.

Qualifications

• 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.

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

Python

SQL

ML libraries

LLMs

MLOps

Data modeling

Tools

Snowflake

PostgreSQL

Vector search

CI/CD

APIs

Job description

Iris client, one of their Insurance Client is looking to hire AI & Data Scientist for Remote.

Job Title: AI & Data Scientist

Location: Remote

Role Type: W2 only

Experience: 15+ years

Position Type: Contract, Right to hire in future

Position Overview

The Senior Applied AI & Data Scientist is a senior-level individual contributor who designs, builds, evaluates, and deploys AI-powered solutions that deliver measurable business outcomes across Life Insurance, Investments, Membership, and Charities. This role blends advanced analytics, machine learning, and generative AI (LLMs) with strong product and engineering execution operating within a governed, enterprise-scale data and AI ecosystem.

You will build solutions using Snowflake, PostgreSQL, and approved AI platforms including Retrieval Augmented Generation (RAG), agentic workflows, and decision support analytics with clear responsible AI controls suitable for regulated environments.

Core 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).

Required:

• 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.