Senior Delivery Manager, Data & Models

Mission Lane

1d•$117,000 - $152,000•Remote

About The Position

Mission Lane is seeking a Senior Delivery Manager, Data & Models to own delivery of data and modeling work from intake through deployment and ongoing maintenance. Reporting to the Senior Director, Portfolio Prioritization & Delivery, this role will establish delivery standards for a newly forming dedicated team and shape how data and model initiatives are planned, sequenced, governed, and delivered across the business. The position coordinates work across data engineering, data science, credit, risk, and compliance. It balances delivery speed with rigor by making dependencies, risks, ownership, and approval requirements visible early. The role also supports model governance readiness, data delivery practices, bank oversight interactions, and continuous improvement of planning and delivery processes. It provides informal guidance to less experienced delivery colleagues. Mission Lane is a purpose-driven U.S. fintech company founded in December 2018 and headquartered in Richmond, Virginia. It offers credit cards through sponsor-bank partnerships. The company describes its workplace as remote-first and emphasizes respect, dignity, unity, empathy, collaboration, and belonging. Mission Lane is an equal opportunity employer and provides reasonable accommodations to applicants as required by law.

Requirements

At least 5 years of experience in program or project delivery, ideally in financial services, fintech, or a data-heavy environment.
Track record leading multiple concurrent initiatives spanning three or more cross-functional teams, including technical teams.
Working knowledge of the data and model development lifecycle, including data engineering, development, validation, deployment, and monitoring, sufficient to plan work and manage dependencies with technical teams.
Experience working within model governance, model risk management, or other formal review and approval processes in a regulated environment.
Experience building delivery structures, processes, or standards—such as intake, documentation, status reporting, and handoffs—that teams adopted and sustained.
Clear, direct written and verbal communication, with unambiguous ownership, status, and next steps.
Experience calibrating escalations to senior leaders, determining what to raise and what to resolve directly.
Experience informally guiding or coaching colleagues without formal management authority.
Ability to travel approximately four or more times per year for in-person collaboration.

Nice To Haves

Direct experience with second-line oversight or bank regulator expectations for model risk management, such as SR 11-7, including preparing for model validation or regulatory examinations.
Experience delivering data platform, pipeline, or analytics initiatives alongside credit risk or other financial models.
Experience establishing a new function or process where no established approach existed.

Responsibilities

Own the delivery roadmap for data and model initiatives across business verticals, including milestones, dependencies, and sequencing, in partnership with the portfolio prioritization team.
Maintain a current consolidated view of initiatives in flight, including status, risks, and next steps, and share it regularly with leaders and partner teams.
Coordinate work across data engineering, data science, credit, risk, and compliance; surface dependencies and unknowns early and drive cross-team issues toward resolution.
Build the delivery playbook for model development, validation, deployment, credit policy updates, and related approval flows, with clear ownership at each handoff.
Ensure models are ready for governance review so required materials and approvals do not become unexpected bottlenecks.
Establish delivery practices for data pipelines, platform changes, data product releases, and data quality remediation, with clear ownership and handoffs.
Serve as an operational point of contact for second-line credit and model risk management bank oversight.
Improve planning and delivery using delivery data and retrospectives to reduce coordination overhead and rework.
Informally guide less experienced delivery colleagues as delivery practices develop.