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About This Role
Managing Principal, AI\-Driven Commercial Effectiveness
ABOUT ODAIA
ODAIA noun
o·da·ia \| \\ 'oh\-day\-yeah \\
An Ancient Greek word referring to “tools of the trade.”
To learn more visit odaia.ai.
ODAIA delivers AI\-powered commercial intelligence for life sciences \- unifying data, accelerating results, and helping commercial teams deepen engagement to enhance care for providers and patients. ODAIA's SaaS platform transforms complex data into predictive, personalized insights, enabling commercial leaders to understand their customers, anticipate prescribing behaviors, and make the informed, strategic decisions that bring therapies to patients faster.
OUR MISSION
Reducing patients’ time to therapy by facilitating meaningful interactions with healthcare providers, through human\-centric software powered by AI.
WHAT’S ON OFFER
For 30 years, the Life Sciences industry has relied on a static analytic, consultant\-led model for sales and marketing effectiveness. This approach relies on "point\-in\-time" snapshots that start decaying the moment they are delivered, leading to billions in wasted spend on the wrong HCPs and missed patient windows. At ODAIA, we are making this antiquated model obsolete. We are evolving our Go\-To\-Market (GTM) motion into a consultative, outcome\-driven model, shifting from a transactional SaaS vendor to a Transformation Partner.
Reporting to the Chief Commercial Officer, as Managing Principal, AI\-Driven Commercial Effectiveness you will bridge the gap between high\-level strategy and real\-time AI execution, architecting executive confidence while ensuring our technology becomes the primary driver of commercial success. Your goal is to move beyond the role of a software vendor to become a vital transformation partner \- helping customers across the pharma landscape find the shortest path to revenue. As an early leader in this evolution, you will define the standards for our pod model, directly shaping ODAIA’s commercial strategy and our continued impact on the industry.
WHAT YOU WILL DO
Lead. Build \& Transform
In this role, you aren't just executing a playbook; you are an architect of our GTM evolution. You will lead a specialist GTM pod, to match the seniority and strategic authority of top\-tier consulting firms while delivering the agility of our AI platform.* Strategic Advisory \& Solutioning to Architect Executive Outcomes
- Act as the trusted advisor to C\-Suite buyers (CCO, CMO, CIO), moving the conversation from "features" to "P\&L impact"
- Lead account level strategy, successfully navigating complex business unit structures and aligning commercial priorities
- Understand and translate complex client pain points into high impact business cases, aligning investment to outcomes (revenue growth, productivity, and competitive advantage)
- Provide solutions that solve specific commercial challenges, positioning ODAIA’s tech as the engine of their transformation, displacing traditional, manual\-heavy consulting cycles
- Provide deep market intelligence and client\-side transformation needs back to our Engineering and Product teams to influence product priorities and solution design to ensure ODAIA remains the premier "tool of the trade"
"Sell\-to\-Deliver" Accountability \& Pod Leadership* Lead a cross\-functional specialist pod, motivated to grow and expand presence within your vertical
- Responsible for segment strategy, focused on driving impact and revenue
- Own the strategic win and success metrics, staying involved post\-close to ensure our "services\-enabled tech" accelerates time\-to\-value for the client
- Define the future GTM strategy, run your market segment like a mini\-business unit, identifying growth triggers to further prove the business case and transition shared resources into dedicated pod structures
WHAT YOU BRING* Consulting Leadership \& Industry Stature
- 15\+ years of experience in Life Sciences / commercial pharma consulting, commercial strategy and / or analytics driven transformation
- Experience in a Principal or Partner\-level role at a top\-tier firm or similar leadership position
- Consistently landed and grown accounts while owning outcomes, has the "consultative DNA" we need to inject into our tech\-heavy environment
- A proven track record of building successful partnerships, advising commercial pharma executives, leading high\-stakes conversations and influencing multi\-million dollar commercial investment decisions through data\-driven storytelling
- Subject Matter Expertise \& Strategic Domain Mastery
- Deep fluency in the Life Sciences commercial ecosystem, including Strategy, Insights \& Planning (SIP), Sales Force Effectiveness (SFE), the application of Patient Longitudinal/Claims data and how to bridge the gap between strategy and execution
- Understanding of modern commercial frameworks, omnichannel strategy, Next Best Action (NBA) orchestration, and how to integrate AI into existing CRM (Veeva/Salesforce) workflows
Operational Agility* Operates as a Player\-Coach and finds the "best yet fastest" actionable answer rather than chasing the "perfect" one
- Operates effectively within a lean, high\-autonomy infrastructure
- A bias for action \- finds moving fast, high\-growth and “re\-building the engine” exhilarating
- Can shift from 30,000\-foot strategy to 3\-foot presentation revision in the same hour
- Has both the experience and confidence to bring clarity to ambiguity and the iterative mindset required to formalize and scale internal commercial frameworks while simultaneously delivering high \- velocity results
WHAT WE OFFER
Values\-Based Culture* We Ignite Innovation, Own It, and Stand Together
AI\-Native Environment* At ODAIA, we don't just deliver AI \- we live it. We use AI and agentic automation to 10x our efficiency and impact, encouraging constant curiosity in leveraging and integrating AI
Comprehensive Rewards* Meaningful stock option grants, immediate medical/dental enrollment, and flexible time off
Remote\-First Flexibility* WFH flexibility with intentional, high\-value in\-person collaboration and socials
LOCATION
ODAIA is a remote first organization, with employees located across Canada and the U.S. Our primary office hub is located in central downtown Toronto and walking distance from Union Station.
EMPLOYMENT VERIFICATION
Any conditional offer of employment made to a successful candidate will be subject to the full satisfaction with the results of any background and reference checks.
JOB PROCESS \& INTERVIEW DISCLOSURE
ODAIA does not use artificial intelligence (AI) to review applications, filter or analyze resumes.
Our recruitment team may use an AI\-powered meeting assistant to record and transcribe interviews for note\-taking purposes only. This tool helps our recruiters to be fully present during conversations with candidates who provide consent to this tool being used during an interview. This tool is used in compliance with privacy and employment laws across Canada and the U.S.
We respect the time candidates invest into participating in our recruitment process. ODAIA is committed to providing timely status updates on hiring decisions to all candidates following their final interview, in accordance with applicable employment laws.
The anticipated base salary for this position ranges from $175,000 \- $275,000\.
This range represents ODAIA's good\-faith estimate based on current market data and internal equity. Final compensation is determined through a comprehensive review of the successful candidate’s unique skill set, specialized experience, certifications and other relevant considerations. As such, the final offer may vary based on these specific factors. Salary expectations will be discussed collaboratively early in the process to ensure alignment. This role is also eligible for health benefits, stock options, and flexible time off policies as mentioned above.
Position Status: Please note that this is a newly created position and not related to an existing position vacancy or departure.
DIVERSITY, EQUITY \& INCLUSION
ODAIA is an equal opportunity employer. We are committed to building an environment where everyone feels included, valued, respected and heard. We are committed to creating a diverse workplace, free from discrimination on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, parental status, veteran status, disability status or any other characteristics protected by local laws, regulations or ordinances.
ACCOMMODATIONS AND ACCESSIBILITY
Accommodations are available upon request. If you need assistance or accommodation due to a disability or special need at any stage of the recruitment process, please contact us at hr@odaia.ai.
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Salary Context
This $175K-$275K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At ODAIA Intelligence Inc., 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($225K) sits 35% above the category median. Disclosed range: $175K to $275K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
ODAIA Intelligence Inc. AI Hiring
ODAIA Intelligence Inc. has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $275K - $275K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% of all AI roles offer remote work.
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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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