Senior Applied AI & Data Scientist

Ampcus Inc; New Haven (CT); On-site; USD 140,000 - 190,000; Full time

Job summary

Ampcus Inc. seeks a Senior Applied AI & Data Scientist to design, build, evaluate, and deploy AI-powered solutions across Life Insurance, Investments, Membership, and Charities.

This senior individual contributor role blends analytics, ML, and generative AI within an enterprise-grade data ecosystem. You will collaborate with stakeholders, data engineers, and AI architects to translate complex questions into explainable, production-ready capabilities using Snowflake, PostgreSQL, and approved AI

Qualifications

• Proficient in Python and SQL with data science best practices.
• Hands-on ML experience with common libraries (scikit-learn, XGBoost, PyTorch/TensorFlow).
• Experience with Snowflake platforms and relational databases (PostgreSQL).
• Familiar with vector search, retrieval patterns, and knowledge retrieval.
• Experience collaborating with engineers on production services, APIs, and CI/CD.

Responsibilities

• Own end-to-end delivery of AI solutions from framing to production deployment.
• Design and deliver LLM-enabled analytics and RAG capabilities over enterprise data.
• Build agentic workflows and orchestration for automation of business processes.
• Develop models for insurance, actuarial, claims, risk, and investment decisions.
• Define evaluation plans and monitor model performance and safety.

Skills

Python

SQL

ML libraries

Vector search

CI/CD

Tools

Snowflake

Snowpark/Cortex

PostgreSQL

Vector databases

Job description

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.

Reporting and Partnerships

Reporting to the Director of Applied AI & Data Science, this role partners closely with business stakeholders, data analysts, engineers, and Data & AI architects to translate complex questions into explainable and production‑ready capabilities. 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).

Skills Qualifications

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.

Preferred