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About This Role
Job Description Summary
We are seeking a Product Strategy Lead to define and drive the vision, roadmap, and delivery of the organization's advanced healthcare data and analytics solutions. This role will serve as a thought leader in healthcare analytics, translating market needs into innovative, sustainable products that deliver measurable value for payers, providers, and other healthcare stakeholders. The Product Strategy Lead will oversee the end\-to\-end product lifecycle, mentor team members, and partner with internal and external stakeholders to ensure the product portfolio remains at the forefront of healthcare data innovation.
This role will also help advance AI\-enabled and agentic product capabilities, translating complex data and analytics needs into intuitive, reliable, and scalable product experiences. The ideal candidate brings experience with AI, machine learning, or data\-driven product environments and can partner effectively across product, engineering, data science, compliance, and business stakeholders to support responsible adoption, customer value, and Plan\-facing readiness.Job Description
- Define and implement the product strategy and roadmap for healthcare analytics portfolio.
- Align product development with market trends, customer needs, and strategic priorities.
- Influence enterprise product vision by contributing insights on competitive positioning, emerging opportunities, and customer pain points.
- Own the full product lifecycle: requirements definition, design, development, testing, launch, and post\-launch optimization.
- Translate business and market needs into actionable product features and analytics solutions.
- Ensure product solutions are differentiated, value\-driven, and aligned with customer outcomes.
- Apply deep healthcare data expertise (claims, payer/provider analytics, value\-based care) to enrich product offerings.
- Identify innovative methodologies and advanced analytics techniques to strengthen BCBSA’s solutions.
- Champion adoption of modern product practices (agile, data\-driven roadmaps, customer co\-design).
- Partner with Finance, Sales, and Marketing on pricing, positioning, and go\-to\-market strategies.
- Lead customer engagements including training, advisory sessions, ROI analysis, and industry forums.
- Build strong relationships with customers, internal stakeholders, and industry thought leaders.
- Mentor product analysts and managers, fostering professional growth and building product leadership capabilities across the team.
- Guide cross\-functional project teams including engineers, data scientists, designers, and architects.
- Ensure adherence to SOC 2, HIPAA, and other compliance standards across product solutions.
- Promote responsible handling of sensitive healthcare data.
The posting range for this position is:
129,600\.00 \- 178,200\.00Education
- Bachelor’s degree in public health, Life Sciences, Computer Science, or related field
- Master’s preferred
Experience
- 10\+ years of experience in product management, with proven success owning roadmaps and delivering healthcare data products.
- Extensive hands\-on experience with payer, provider, and pharmacy analytics, including claims data.
- Experience leading or supporting AI, machine learning, advanced analytics, data platform, or agentic product capabilities in a healthcare, payer, provider, or enterprise data environment.
- Experience partnering with engineering, data science, compliance, security, and business teams to evaluate product tradeoffs, manage risk, and support scalable deployment of data\-driven or AI\-enabled solutions.
- Experience working on a product through a 0\-to\-1 or early\-growth phase.
- Preferred: Experience with AI\-enabled analytics products, agentic AI platforms, natural language interfaces, or products that enable non\-technical users to access, interpret, or act on complex data.
Knowledge, Skills and Abilities
- Strong knowledge of population health management, value\-based care programs, and healthcare ecosystem trends.
- Demonstrated ability to translate complex analytics, data science, or AI\-enabled capabilities into clear product requirements, user workflows, adoption strategies, and measurable customer outcomes.
- Track record of developing innovative, data\-driven solutions that drive measurable customer and business outcomes.
- Proven ability to influence executives, customers, and cross\-functional teams.
- Excellent communication and presentation skills, with experience representing products in customer and industry settings.
- Familiarity with compliance frameworks (SOC 2, HIPAA) and secure product practices.
- Working knowledge of modern AI concepts, including generative AI, large language models, agents, retrieval, prompting, and responsible AI practices, with the ability to apply these concepts to product strategy and decision\-making.
- Ability to assess AI\-enabled product opportunities through the lens of customer value, usability, reliability, data quality, security, compliance, and measurable business impact.
- Strong ability to translate between technical teams, business leaders, product teams, and end users to ensure AI\-enabled solutions are understandable, usable, and aligned to customer outcomes.
- Familiarity with enterprise data ecosystems, analytics platforms, and cloud\-based data environments such as AWS, Snowflake, Databricks, or similar platforms.
- Ability to promote responsible handling of sensitive healthcare data, including awareness of AI\-specific risks, data sensitivity considerations, transparency, and appropriate human review.
\#LI\_HYBRID
The posted salary range is the lowest to highest salary we, in good faith, believe we would pay for this role at the time of this posting. We may ultimately pay more or less than the hiring range and this hiring range may also be modified in the future. A candidate’s position within the hiring range may be based on several factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, relevant experience, skills, seniority, performance, shift, travel requirements, and business or organizational needs. This job is also eligible for *annual* *bonus**incentive*pay.
We offer a comprehensive package of benefits *including paid* *time off,* *11 holidays,medical/dental/vision* *insurance,* *generous* *401(k)* *matching,* *lifestyle spending account* *and* *m**any other benefits* to eligible employees.
Note: No amount of pay is considered to be wages or compensation until such amount is earned, vested, and determinable. The amount and availability of any bonus, commission, or any other form of compensation that are allocable to a particular employee remains in the Company's sole discretion unless and until paid and may be modified at the Company’s sole discretion, consistent with the law.
Salary Context
This $129K-$178K range is below 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 Blue Cross Blue Shield, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($153K) sits 30% below the category median. Disclosed range: $129K to $178K.
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.
Blue Cross Blue Shield AI Hiring
Blue Cross Blue Shield has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $178K - $178K.
Location Context
AI roles in Chicago pay a median of $205,100 across 97 tracked positions. That's 6% below the national 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 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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