Business Operations Manager, Men’s Health

LifeMD•New York, NY

About The Position

LifeMD is a leading provider of virtual primary care, telehealth, and specialized treatment programs. The Business Operations Manager will serve as a primary operational partner to the General Manager of LifeMD’s Men’s Health business. This role involves leading the end-to-end execution of new product launches, developing and managing the vendor and partner ecosystem, and leveraging AI to deliver outcomes. This is a hands-on execution role focused on direct delivery and ownership of results, rather than a coordination function. The manager will partner with the GM on strategy while leading execution across a diverse product portfolio with unique clinical, regulatory, supply chain, and growth considerations.

Requirements

Three to six years in a high-execution role such as business operations, strategy and operations, founding-team operator, chief of staff, product operations, or management consulting (post-MBB/IB), ideally within a high-growth startup, health tech company, or DTC brand.
A demonstrated track record of personally delivering work—products, processes, vendor relationships, and internal tools—rather than solely managing it.
Technical literacy, including comfort reading API documentation, writing SQL for ad-hoc analysis, troubleshooting vendor integrations, prototyping in no-code.
Strong quantitative skills KPI definition and tracking, and a working knowledge of statistics sufficient to assess analytical quality.
Fluency with AI tools, including daily use of LLMs for substantive work, informed perspectives on model selection, prompting, and evaluation, and at least one self-built workflow that delivered measurable time savings.
Excellent written and verbal communication—concise, direct, and conclusion-oriented—with the ability to engage credibly with a GM, a clinical advisor, and a pharmacy account manager alike.
Comfort with ambiguity and a strong bias toward action, with the ability to define under-specified problems and then solve them.

Responsibilities

Lead New Product Launches End-to-End: Guide products from approval through to live, converting offerings, sequencing work across clinical, regulatory, product, marketing, supply chain, and pharmacy functions. Own the launch plan, milestones, and dependencies, surfacing blockers early and resolving gaps directly when ownership is unclear. Conduct a retrospective after each launch, identify the highest-priority improvements, and implement them before the next launch.
Build and Manage the Vendor and Partner Ecosystem: Source, evaluate, contract, onboard, and manage performance across pharmacies, lab partners, EHR and telehealth platform vendors, payment processors, lifecycle/CRM tools, and AI and automation providers.
Translate Strategy into Execution: Convert the GM’s priorities into concrete plans with clear owners, timelines, and measurable outcomes, then drive them to completion. Maintain a disciplined weekly cadence with the GM covering progress, risks, and decisions requiring escalation.
Build with AI: Use AI tools such as Claude, ChatGPT, Glean, and Notion AI as core infrastructure rather than novelty—prototyping internal workflows, specifying automations for engineering, and developing code, SQL, or no-code integrations where doing so accelerates delivery. Identify opportunities where AI can consolidate multi-step manual processes—such as patient-intake review, vendor outreach, claims and compliance summarization, and content QA—and build the prototypes.
Serve as the Operational Subject-Matter Expert: Maintain a thorough understanding of the competitive landscape, patient journey, pricing, and channel-level conversion economics.
Own KPIs, Financial Modeling, and Performance Analytics: Maintain the KPI dashboard for each product line—acquisition, activation, conversion, AOV, retention, churn, refill rate, CAC/LTV, vendor unit cost, and gross and contribution margin—setting targets with the GM and reporting against them weekly. Apply rigorous analytical methods—A/B test design and sizing, statistical significance and confidence intervals, cohort analysis, retention curves, and regression—to distinguish genuine signal from noise. Identify operational inefficiencies such as slow vendor handoffs, manual reconciliation, and compliance gaps, then redesign and implement improvements.