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About This Role
Head of Client Success & Growth
Reports to
Chief Commercial Officer
Employment Type
Full-Time Independent Contractor
Location
Remote / Offshore
Schedule
Monday–Friday, 9:00 AM–5:00 PM Eastern Time
Compensation
$50-$70k USD annually, based on experience and qualifications, with the opportunity to earn additional performance-based bonuses tied to commercial and growth results.
About Ironsail Pharma
Ironsail Pharma is a B2B healthcare platform connecting healthcare providers and organizations with a network of pharmacy partners. Through our ImpetusOne platform, clients explore medication catalogs and pharmacy options, submit and track orders, and coordinate fulfillment across patient-specific and direct-to-clinic services. Our clients include independent medical practices, med spas, wellness and weight-management clinics, and telehealth companies. We work with prescribing providers, practice owners, clinical and administrative teams, and commercial decision-makers.
About the Role
We are seeking a Head of Client Success & Growth to own Ironsail Pharma's day-to-day commercial client function and help build a high-performing, accountable, AI-enabled team.
This is a hands-on commercial leadership role responsible for both client success and commercial growth: provider activation, client retention, reactivation, account expansion, and growth. You will combine strategic leadership with direct client involvement, operational execution, and AI-driven process improvement.
A key expectation is the ability to build and lead efficient, AI-assisted workflows that reduce repetitive work, improve client outcomes, and support growth without proportional increases in manual workload. We are looking for someone who can move beyond basic AI usage to help design, implement, evaluate, and continuously improve AI-powered business processes.
You will directly manage the Provider Implementation Specialist, work closely with the Account Executive, and partner across Operations, Finance, Marketing, Product, and Development.
Key Responsibilities
Client Success & Commercial Growth
- Own the client journey from onboarding and first order through ongoing account management, retention, reactivation, and expansion.
- Develop proactive account strategies based on client activity, value, needs, and growth potential, and pursue opportunities to increase account activity, product adoption, and overall client value.
- Lead commercial conversations about products, pharmacy options, pricing, and service needs within approved parameters.
- Identify stalled, declining, or inactive accounts and build clear plans to remove barriers and re-engage clients.
- Partner with the Account Executive on account planning, opportunities, and expansion.
- Leverage client health indicators, AI-assisted analysis, and automated monitoring to proactively identify churn risks and account growth opportunities.
Team Leadership & Implementation
- Directly manage the Provider Implementation Specialist, including priorities, workload, coaching, one-on-ones, follow-through, and performance management.
- Oversee implementation from qualified handoff through onboarding, first order, and early account activity.
- Build practical playbooks, follow-up standards, workflows, and escalation paths.
- Create clear accountability so the client function operates effectively without constant executive direction.
- Establish clear service expectations, escalation procedures, and performance standards for client-facing activities, coordinating with Operations and other responsible teams.
- Identify opportunities to automate repetitive client service and administrative tasks while maintaining appropriate human oversight and accountability.
CRM & Commercial Operations
- Own the commercial use of Pipedrive, including pipeline stages, account ownership, activities, handoffs, next steps, reporting, and useful automations.
- Drive consistent CRM adoption and maintain accurate prospect, onboarding, and active-client records.
- Monitor overdue activities, stale opportunities, missing information, and unassigned leads.
- Partner with Operations on CRM integrations and process improvements.
- Develop AI-assisted workflows for account health monitoring, client segmentation, follow-up prioritization, reporting, and identification of retention or expansion opportunities.
- Collaborate with technical teams to implement and improve automated workflows connecting CRM data, client activity, and operational processes.
Marketing & Client Communications
- Partner with Marketing on lead quality, conversion feedback, campaigns, and client-facing communications.
- Review routine Meta and Google advertising materials within approved brand and messaging guidelines.
- Help ensure website content, offers, landing pages, calls to action, and lead-capture processes accurately reflect current capabilities.
- Oversee recurring client newsletters, including content planning, accuracy, approvals, and performance.
- Partner with Product and Development to translate confirmed software releases into clear client communications and adoption messaging.
Growth, Client Feedback & Performance
- Turn commercial priorities into actionable growth initiatives with clear owners, timelines, and measurable outcomes.
- Collect and analyze client feedback through onboarding check-ins, account reviews, surveys, and outreach.
- Identify recurring reasons clients stall, reduce activity, or leave, and coordinate solutions across teams.
- Monitor key commercial metrics, including lead conversion, onboarding progress, time to first order, active accounts, retention, reactivation, expansion, order activity, and revenue trends.
- Conduct weekly and monthly performance reviews and translate findings into clear actions and recommendations.
- Track relevant client service and operational metrics, including response times, resolution times, escalation trends, and service quality.
- Evaluate the effectiveness of AI-assisted workflows through measurable improvements in accuracy, automation, efficiency, client retention, and team productivity.
AI-First Environment
Ironsail operates with an AI-first mindset. Practical, professional use of AI is an everyday expectation of this role, not an optional skill.
We are specifically seeking someone with hands-on experience implementing or leading AI-powered business workflows, rather than relying solely on AI for writing, research, or basic productivity tasks.
You will use approved AI tools to improve:
- Client communications and account research
- Reporting and trend analysis
- CRM workflows and follow-up
- Client segmentation, churn-risk identification, and growth analysis
- Process automation and team productivity
- Client service workflows, issue prioritization, and escalation management
- Automated account monitoring and identification of opportunities requiring human intervention
You will also be expected to:
- Identify opportunities to automate repetitive client-facing and operational processes.
- Design, implement, or directly oversee AI-assisted workflows in collaboration with technical teams.
- Understand how AI agents and automated workflows can support client operations, including where human intervention is required.
- Evaluate AI workflow performance using measurable indicators such as accuracy, time savings, automation effectiveness, and escalation rates.
- Continuously improve AI-assisted processes while ensuring service quality and operational accountability.
- Help team members adopt effective AI-assisted workflows and maintain clear standards for their use.
AI outputs must be reviewed and verified, client and patient information must be carefully protected, and human accountability remains for all decisions and external communications. Clinical, compliance-sensitive, or other high-risk matters must be escalated to appropriately authorized personnel.
What We're Looking For
- Demonstrated ownership of a B2B client success, account management, or commercial function.
- Direct people-management experience with responsibility for team performance and accountability.
- Proven results in client retention, reactivation, expansion, account growth, or revenue growth.
- Strong commercial judgment and confidence communicating directly with clients and decision-makers.
- Strong CRM experience, preferably Pipedrive or a comparable platform, including reporting, workflows, and team adoption.
- Demonstrated hands-on experience implementing, configuring, or directly leading AI-powered business workflows beyond basic prompting, content creation, or research.
- Ability to provide specific examples of AI workflows implemented or managed, including tools used, business problems addressed, level of automation, and measurable results.
- Practical understanding of how AI can improve client service operations, CRM processes, client health monitoring, reporting, and operational efficiency.
- Experience establishing or improving client service workflows, performance standards, escalation processes, and operational accountability.
- Ability to collaborate with technical teams to design, evaluate, and improve AI-driven workflows without necessarily being a software engineer.
- Experience working cross-functionally with Sales, Operations, Marketing, Finance, and Product/Development teams.
- Strong written communication skills and the ability to oversee client-facing communications.
- Comfort staying hands-on with accounts while building and leading a growing function.
Strongly preferred:
Experience in B2B healthcare, health technology, pharmacy services, telehealth infrastructure, provider-facing software, med spas, or wellness.
Additional preferred experience includes AI-agent workflows, CRM automation, automated client health scoring, customer service operations, SLA management, and AI-assisted support processes.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Ironsail Pharma, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Ironsail Pharma AI Hiring
Ironsail Pharma has 1 open AI role right now. They're hiring across AI/ML Engineer.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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