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PulsePoint
Director of Product Management, Intelligence Group
Data Science \& ML Optimization
About the Role
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PulsePoint is seeking a Director of Product Management to lead our Intelligence Group, the team responsible for the machine learning, identity, targeting, and data science capabilities that power our healthcare programmatic advertising platform. This is a senior leadership role combining deep technical product ownership with commercial vision, sitting at the intersection of data science engineering and ad tech.
The Intelligence Group owns five product streams: a real\-time programmatic optimization engine, AI\-powered targeting intelligence, identity resolution and taxonomy infrastructure, third\-party data partnerships, and campaign forecasting. The team translates complex ML models and clinical data signals, including pharmaceutical prescribing activity, diagnosis patterns, and healthcare professional engagement data, into commercially valuable ad products used by the world's leading pharmaceutical and healthcare brands.
This role demands someone with a strong foundation in ad tech optimization algorithms, the ability to build trusted relationships with data science researchers, and the commercial instinct to turn those capabilities into revenue\-generating products. You will manage a team of product managers across five streams and report to the Chief Product Officer.
Key Responsibilities
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ML Optimization \& AI Platform
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- Own the full product strategy for our real\-time programmatic optimization engine: factor design methodology, scoring logic architecture, bid optimization models, package configuration, data science workflows, and A/B testing framework.
- Drive product ownership of our internal performance analytics and AI platform, scaling self\-service capabilities for campaign intelligence, audience quality monitoring, and optimization model performance tracking.
- Lead evaluation and productization of emerging optimization capabilities, including clinical outcome measurement integrations (script lift, office visit lift), cost\-per\-qualified\-reach models, and unsupervised optimization research.
- Lead the evaluation and RFP process for clinical claims data providers (pharmacy, diagnosis, and procedure data), ensuring resilience and quality of the data signals that power our optimization models.
- Act as the strategic gatekeeper and triage lead for AI/ML feature prioritization, coordinating incoming data science requests across all product streams and external commercial partners.
AI Targeting, Identity \& Taxonomy
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- Own our AI\-powered targeting recommendation engine and direct real\-time contextual AI integrations for pharmaceutical drug, disease context, and AI\-driven medical publisher targeting environments.
- Lead identity infrastructure modernization including migration to next\-generation vendors (Experian, UID2\.0, ID5\), unification of identity architecture across PulsePoint and WebMD/Medscape publisher properties, and assessment of major industry identity shifts on onboarding and clean\-room capabilities.
- Own migrations to modern taxonomy frameworks (IAB 3\.1, MeSH 2025\) and NPI (National Provider Identifier) level targeting and reporting infrastructure for healthcare professional campaigns.
Data Partnerships, Audience Products \& Forecasting
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- Manage strategic data vendor relationships across verification, demographic enrichment, identity onboarding, and clinical outcomes measurement; own the product side of integrations with major healthcare data providers.
- Oversee specialized audience products: healthcare professional (HCP) targeting, decision\-maker and executive audiences, direct\-to\-consumer bespoke segments, and NPI\-based provider audience management.
- Own the campaign forecasting product, providing pre\-campaign audience size estimates, delivery projections, and inventory availability data for sales and campaign planning workflows.
People Management \& Commercial Enablement
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- Lead, mentor, and grow a multi\-stream team of product managers across optimization, identity and targeting, data partnerships, forecasting, and AI foundation streams.
- Partner with sales, GTM, and data science engineering to translate complex ML capabilities into commercially sellable frameworks, client\-ready narratives, and field enablement tools.
- Represent the Intelligence Group roadmap in executive, board, and client\-facing contexts; serve as the internal subject matter expert for data science product capabilities.
Qualifications
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Required
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- 8\+ years in Product Management with a track record of scaling high\-complexity platform products, data infrastructure, or ML/AI products.
- Strong background in ad tech optimization algorithms: real\-time bidding logic, bid price optimization, CTR/CVR prediction models, pacing and delivery algorithms, or ML\-driven campaign performance optimization at scale.
- Deep familiarity with AI/ML workflows: scoring logic frameworks, A/B testing methodologies at scale, model evaluation approaches, and predictive modeling lifecycles.
- Experience with programmatic advertising data flows, audience targeting mechanics, and identity resolution in a DSP, SSP, or data platform environment.
- Proven experience managing external data integrations, measurement vendors, or third\-party data partnerships including commercial negotiation.
- 2\+ years of direct people management of Product Managers in a fast\-paced, cross\-functional environment.
- Strong executive presence; able to translate dense technical concepts into clear commercial value propositions for senior stakeholders, sales teams, and external clients.
Preferred
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- Background in AdTech, healthcare or pharmaceutical data environments (HCP targeting, NPI data, clinical claims), or large\-scale B2B SaaS data products.
- Experience with programmatic identity infrastructure: LiveRamp, UID2\.0, ID5, Experian, or equivalent next\-generation identity vendors.
- Familiarity with clinical or pharmaceutical data signals: pharmacy (Rx) claims, ICD\-10 diagnosis codes, CPT procedure codes, or NDC drug codes.
- Familiarity with healthcare compliance and privacy\-safe data environments including HIPAA, de\-identification standards, and data clean\-room technology.
Benefits
- Comprehensive healthcare with medical, dental, and vision options, and 100%\-paid life \& disability insurance
- 401(k) Match
- Generous paid vacation and sick time
- Paid parental leave \& adoption assistance
- Annual tuition assistance
- Better Yourself Wellness program
- Group volunteer opportunities and fun events
- Commuter benefits and commuting subsidy
- A referral bonus program. We love hiring referrals here at PulsePoint
- You can work remotely
And there’s a lot more! Salary Range* $200\-205k base \+ $20\-20\.5k performance bonus based on company and individual performance
Selection Process (may be subject to change):* Initial Phone Screen (30 minutes)
- Hiring Manager Interview (30 minutes)
- Team Interview (2\-4 team members for 30 minutes each)
- Case Study
- IB Executive Conversations (1\-2 executives, 30 minutes each)
- Reference Checks (2\-3 references)
WebMD and its affiliates is an Equal Opportunity/Affirmative Action employer and does not discriminate on the basis of race, ancestry, color, religion, sex, gender, age, marital status, sexual orientation, gender identity, national origin, medical condition, disability, veterans status, or any other basis protected by law.
Salary Context
This $200K-$205K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At PulsePoint, 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($202K) sits 7% below the category median. Disclosed range: $200K to $205K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
PulsePoint AI Hiring
PulsePoint has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $205K - $205K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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