Interested in this AI/ML Engineer role at Big Sky Care Connect?
Apply Now →Skills & Technologies
About This Role
Location: U.S., Montana strongly preferred (remote with travel)
Status: Full-time, regular, FLSA exempt
Reports to: Vice President – Program & Services
Organizational Overview:
Healthcare stakeholders formed Big Sky Care Connect (BSCC), a 501(c)(3) nonprofit corporation, in 2018 to operate a health information exchange (HIE) in Montana. BSCC enables the secure sharing of vital healthcare information to improve the speed, quality, safety, coordination, and cost of patient care. BSCC serves as a state-wide network and repository for patient health information, including comprehensive clinical and claims data, and offers HIE-related services to the Montana healthcare community, connecting providers, payors, and patients.
Position Overview:
The Director of Analytics & Data Science leads BSCC's analytics platform and team, drives internal data science research to advance BSCC's methodology and analytic capabilities, and works directly with provider, payor, and state partners to deliver and develop analytics and data products that create value across the statewide HIE network. The position balances hands-on technical leadership, applied research, and partner-facing delivery to strengthen BSCC's role as Montana's trusted healthcare data convener.
Essential Functions:
Duties A – C below are fundamental to achieving the role's objectives and must be performed with or without reasonable accommodation.
- Analytics Platform & Team Management
Manage BSCC's analytics platform and the analytics team to ensure reliable, high-quality, and scalable delivery of data and analytics services across the organization.
- Oversee the day-to-day operation, maintenance, and roadmap of BSCC's analytics platform, ensuring data quality, availability, and performance meet organizational and partner needs.
- Lead, coach, and develop analytics team members, setting clear priorities, performance expectations, and professional growth plans.
- Establish and maintain standard workflows, documentation, and quality-control processes for analytics development and platform operations.
- Manage analytics platform vendor relationships, licensing, and technical roadmap decisions in coordination with the Technology & Operations team.
- Set and manage team priorities, workload, and capacity planning to ensure timely delivery of analytics projects and platform enhancements.
- Establish metrics and reporting to track platform performance, team productivity, and delivery timelines; use these metrics to drive continuous improvement.
- Partner with the BSCC executive team on budget planning, staffing needs, and resource allocation for the analytics function.
- Ensure analytics platform practices align with applicable data governance, privacy, and security requirements (e.g., HIPAA).
- Data Science Research
Conduct and guide internal data science research to strengthen BSCC's analytic methodology, expand the organization's data product capabilities, and generate insights that support Montana's healthcare community.
- Design and conduct original analyses (e.g., predictive modeling, risk stratification, population health trends) that advance BSCC's data science capabilities.
- Evaluate and adopt new analytic methods, tools, and technologies applicable to health information exchange and population health analytics.
- Translate research findings into practical, deployable analytics products and recommendations for internal and partner use.
- Support grant-funded or externally funded research and evaluation initiatives as opportunities arise.
- Maintain rigor and reproducibility in research methodology, including appropriate documentation and validation of analytic models.
- Stay current on emerging trends in health data science, interoperability standards, and analytics best practices relevant to BSCC's mission.
- Partner & Participant Analytics Delivery
Work directly with BSCC's provider, payor, and state partners to deliver and develop analytics and data products that meet partner needs and strengthen BSCC's value proposition.
- Serve as the primary analytics point of contact for assigned partners and participants, understanding their data and reporting needs.
- Collaborate with partners to design, develop, and refine dashboards, reports, and data products that support their clinical and operational goals.
- Gather and incorporate partner feedback to improve data quality, platform usability, and analytics service delivery.
- Present analytic findings and recommendations to partner organizations in clear, actionable terms for both technical and non-technical audiences.
- Coordinate with BSCC's Outreach and Program teams to identify opportunities to expand analytics offerings and partner engagement.
- Support onboarding of new partners and participants onto BSCC's analytics platform and services.
- Other duties as assigned
Perform other duties as assigned to support BSCC operations. This includes but is not limited to filling in for other leaders, directing special projects and events, and attending training and education.
Qualifications:
Requires education and experience equivalent to a master's degree in data science, statistics, health informatics, public health, computer science, or a related field, and five years of experience in data science, analytics, or a related field, including experience leading a team or platform. A PhD in a relevant quantitative or health-related field may substitute for a portion of the required experience.
Preferred qualifications:
Experience in a health information exchange (HIE), health information network, or healthcare analytics environment; supervisory or people-management experience; proficiency with statistical programming (e.g., R, Python, SQL) and data visualization tools; knowledge of healthcare data exchange, privacy, and regulatory requirements (e.g., HIPAA, data governance); familiarity with the Montana healthcare landscape.
Working Conditions and Physical Demands
Position works remotely within the United States and requires the ability to effectively use office equipment and technology.
Requires the ability to travel throughout Montana by automobile and travel nationally by commercial air to attend meetings, conferences, and partner engagements on an occasional basis.
The position requires the ability to communicate effectively, participate in meetings, and perform work in a professional office or virtual environment. Qualified candidates can perform the essential functions of the position with or without reasonable accommodation.
Key Competencies (knowledge, skills, abilities, and behaviors):
Knowledge (what BSCC leaders must understand)
- Data science methodology: statistical modeling, predictive analytics, and research design applicable to health data.
- Analytics platform operations: data pipelines, quality assurance, and platform reliability.
- Healthcare data standards: clinical and claims data structures, interoperability standards, and data governance.
- Quality and performance fundamentals: core concepts of quality, reliability, access, and service experience.
- Data and measurement: leading vs. lagging indicators; how to interpret trends and separate signal from noise.
- Stakeholder ecosystems: how to work effectively with executives, internal teams, vendors, and partner organizations.
- People leadership and culture: accountability systems, engagement drivers, and psychological safety.
Skills (what you must be able to do)
- Analytic and technical execution
- Design, build, and validate statistical and predictive models.
- Translate complex analytic findings into clear, actionable recommendations.
- Team and platform management
- Set priorities, manage workload, and hold the team accountable to delivery timelines.
- Oversee platform performance, vendor relationships, and technical roadmap decisions.
- Communication and influence
- Communicate clearly with executives, technical teams, and external partners.
- Build alignment through compelling narrative and credible data.
- Partner and stakeholder management
- Build trust-based relationships with provider, payor, and state partners.
- Manage expectations, gather feedback, and translate it into service improvements.
- Performance management
- Establish dashboards and review cadences; drive corrective action when needed.
- Diagnose performance gaps and focus the team on the few actions that matter most.
- Talent development
- Coach team members; give timely feedback; set clear expectations.
- Delegate effectively and build bench strength.
Abilities (what BSCC leaders can reliably handle)
- Analytical rigor under ambiguity: design sound approaches with incomplete data and adapt quickly as conditions change.
- Decision making and prioritization: integrate input, manage risk, and move work forward without delay.
- Systems thinking: anticipate downstream impacts across people, process, technology, and incentives.
- Accountability with compassion: hold high standards while meeting people as whole humans.
- Change leadership: create clarity, pace change appropriately, and build commitment through engagement.
- Executive presence and credibility: stay steady under pressure; communicate with confidence and humility.
- Integrity and values alignment: align actions with values, protect confidentiality, and follow through on commitments.
Behaviors (How we expect BSCC leaders to show up each day)
- Creates clarity: sets clear priorities and reinforces them consistently; creates role clarity and decision rights.
- Runs an operating system: runs disciplined operating cadences (weekly metrics review, project check-ins, quarterly planning).
- Leads with presence: communicates in truth always, directly, and respectfully; addresses issues early.
- Turns strategy into action: develops executable plans with clear deliverables, milestones, and accountability.
- Leverages data: uses data as a learning tool (not a weapon) and tests assumptions.
- Builds partnerships: builds partnership across stakeholders; resolves conflicts early and makes tradeoffs transparent.
- Develops people: develops people through coaching, feedback, and meaningful delegation.
- Continuous improvement: sponsors continuous improvement and sustains gains over time.
- Models integrity: keeps commitments; owns mistakes; protects confidentiality and maintains a calm leadership presence.
Work Authorization & Location Requirements: Incumbent must be authorized to work in the United States on a full-time basis. BSCC does not sponsor employment visas. This position is remote within the United States; candidates must reside and perform work within the U.S.
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Big Sky Care Connect, 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 $212,000 based on 11,866 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Big Sky Care Connect AI Hiring
Big Sky Care Connect has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.