Forward-Deployed Actuary, Client Delivery

Accorded

49d•Remote

About The Position

We are seeking an experienced Actuary to join our lean actuarial team in a client-facing delivery role. This position involves supporting a portfolio of active accounts by running analyses, building and pressure-testing models, and communicating results directly to client stakeholders. The role requires a blend of analytical and relational skills, with the potential to take primary ownership of selected accounts over time. This is an active, client-facing role, not a back-office position.

Requirements

ASA designation required; FSA a plus.
3+ years of actuarial experience in health (5+ preferred); consulting or payer background both welcome.
Meaningful exposure to value-based care, population health analytics, or Medicare Advantage.
Excel modeling: fluency with complex workbooks.
SQL / BigQuery: able to write and debug queries independently against large claims datasets.
AI tool proficiency: actively use AI tools to accelerate work.
Clear communicator — written and verbal — with non-actuarial audiences.
Comfortable owning a deliverable under time pressure without a playbook to follow.
Organized enough to manage multiple concurrent client workstreams without dropping threads.
Curious, not just compliant — want to understand why the model says what it says.
Strong written communication and self-direction are non-negotiable.

Nice To Haves

FSA designation
5+ years of actuarial experience in health
Familiarity with common claims data sources (MarketScan, Komodo, CMS-RIF, or similar)

Responsibilities

Execute client-facing actuarial analyses across core methodologies: dynamic benchmarking, propensity score matching, and performance guarantee modeling.
Build, validate, and explain financial models grounded in claims data (Medicare Advantage, commercial, and Medicaid).
Own deliverables end-to-end: from data pull in BigQuery to a client-ready output that tells a coherent story.
Participate in client calls and presentations, translating complex actuarial concepts for clinical and operational audiences.
Support scoping and methodology design for new engagements, including SOW input and assumptions documentation.
Collaborate with data engineering and data science colleagues to improve model infrastructure and delivery pipelines.
Identify gaps, surface risks, and propose solutions proactively.