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Manager, Consulting – AI, Pharma \& Life Sciences (US Landing Team)
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Location: United States (Remote / East Coast Preferred / Hybrid Client Travel)
Position Type: Full\-Time
Practice Area: Data, AI \& Digital Strategy — Pharma \& Life Sciences
About Converteo
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Converteo is a leading hybrid consulting firm at the intersection of business strategy, data architecture, and Agentic AI. We help global enterprise leaders navigate complex digital transformations, turn massive data assets into actionable commercial advantage, and deploy production\-ready AI solutions.
With deep European roots and an expanding footprint across North America, Converteo combines boardroom\-level strategic advisory with hands\-on technical execution. We work alongside top\-tier global enterprise and pharmaceutical clients to build the future of AI\-driven business models.
As part of our initial US Market Capture Team, you will play a pivotal, high\-impact role in embedding Converteo’s strategic AI methodologies into the world’s most dynamic pharmaceutical, biotech, and healthcare ecosystems.
The Role \& Strategic Impact
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As a Manager, AI Consulting (Pharma \& Life Sciences) on our US Landing Team, you will bridge the gap between complex C\-suite strategic mandates and modern, agentic AI deployment. You will advise executive leadership across major US pharma and life science hubs on high\-stakes initiatives—from AI governance and data infrastructure to commercial AI roadmaps and drug lifecycle optimizations.
In this role, you will lead end\-to\-end client engagements, articulate complex AI architectures to non\-technical stakeholders, and help shape Converteo’s permanent, high\-velocity presence in North America.
What You Will Own
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- Strategic AI Advisory: Collaborate directly with C\-suite and VP\-level stakeholders across Pharma and Life Sciences to answer high\-level questions: How do we operationalize Agentic AI safely? How do we modernize our data governance for strict healthcare compliance? Which AI use cases drive immediate ROI in commercial and clinical operations?
- Structured Problem\-Solving: Break down complex, multi\-layered healthcare challenges into actionable strategic roadmaps. Combine classic strategy consulting frameworks with Converteo’s proprietary data and AI toolsets.
- End\-to\-End Engagement Delivery: Own project workstreams from hypothesis framing and market intelligence to executive workshops, governance design, implementation roadmaps and high quality deliverables.
- Client Engagement \& Advisory: Build deep, trusted advisor relationships with enterprise business unit leaders. Translate complex technical concepts into clear, high\-polish visual presentations and business cases.
- Practice Building \& US Market Growth: Act as an early pioneer for Converteo’s US footprint. Contribute to business development, author localized intellectual capital (whitepapers, frameworks, benchmarks), and mentor incoming team members.
Who You Are
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- Professional Experience: 6\-10 years of experience in strategy consulting, data/digital advisory, or business analytics, with a mandatory focus on the Pharma, Biotech, or Life Sciences sectors.
- Pharma Domain Expertise: Strong grasp of the US life sciences ecosystem—including FDA regulatory frameworks, healthcare procurement, the drug development lifecycle, and commercial go\-to\-market strategies.
- Dual Affinity (Business Acumen \+ AI Fluency): High comfort level operating between executive strategy and modern technology. You don't need to write production backend code, but you must be fluent in data pipelines, analytics, machine learning concepts, enterprise cloud platforms, and modern AI/LLM applications.
- High\-Polish Communication: Exceptional written and verbal communication skills. You possess the visual taste and narrative structure needed to deliver boardroom\-ready presentations for US enterprise buyers.
- Analytical Precision: Strong quantitative skills with a track record of deriving clear, high\-impact business insights from complex data sets.
- Education: Bachelor’s or Master’s degree in Business, Computer Science, Engineering, Economics, or a related quantitative field.
- Work Authorization: Must be fully authorized to work in the United States.
Why Join Converteo’s US Launch?
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- Strategy with a Tactical Execution Edge: We aren't a dry, theoretical agency, nor are we a pure IT shop. We sit at the exact intersection of executive strategy and rapid AI deployment.
- Pioneer Status: As an early member of our US team, you will have direct visibility with global leadership from Day 1, shaping our North American playbook and footprint.
- High\-Velocity Culture: We combine deep European analytical rigor with an aggressive, entrepreneurial US growth mindset.
- Career Trajectory: Unmatched opportunity for fast\-tracked leadership growth as our US practice expands across major tech and pharmaceutical verticals.
### Compensation
- Base Salary: $125,000 – $155,000 USD (Depending on geographic zone and experience).
- Benefits: 20 days PTO, winter closure, 11 paid holidays, Summer Fridays, 401K Match
Salary Context
This $125K-$155K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Converteo, 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 in Demand for This Role
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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($140K) sits 35% below the category median. Disclosed range: $125K to $155K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Converteo AI Hiring
Converteo has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $155K - $155K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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