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About This Role
POSITION SUMMARY:
The Financial Analyst – AI \& Data Analytics will support a dental practice management software platform by applying advanced financial analysis, automation, and AI\-driven insights to subscription revenue, practice usage, retention and operational performance. The position will be charged with implementing solutions to automate processes and reporting, development of advanced analytics and improving dexterity of data to include a comprehensive view of customers. These efficiencies should result in replicable data, reporting and self\-service for business managers. This role partners with Product Management, Sales, Customer Success, and Controllership to improve forecasting, revenue and cost analysis and data\-driven decision\-making across SMB and DSO customers.
KEY/SPECIFIC JOB RESPONSIBILITIES:
*FP\&A \& SaaS Performance*
- Build and maintain financial models for ARR, MRR, bookings, churn, net revenue retention and upsell
- Support monthly forecasting, annual budgeting, and long\-range planning for revenue, cost of sales and operating expenses
- Prepare monthly financial reports for management and executive level review
- Analyze revenue performance by practice size, DSO vs. SMB, and region
Partner with Accounting to ensure alignment with ASC 606 for software subscriptions, implementation fees, and support contracts
*
*AI, Automation \& Predictive Analytics*
- Develop AI\-enabled forecasting models using pipeline, usage, and renewal data to:
+ Predict churn, downsell and volume
+ Identify upsell and cross\-sell opportunities with specific cohorts, software versions and attachment characteristics
+ Improve forecast accuracy using practice volume, activity and appointment data
- Automate recurring finance processes (forecast updates, variance analysis, KPI reporting) using:
+ Power BI or similar tools
+ Microsoft Copilot or similar tools
Automate operational analysis supporting various functional areas of the business and ensure it is self\-service and on\-demand for business managers
*
*Practice Usage \& Operational Analytics*
- Analyze practice management workflows (appointments, scheduling, billing, claims, reminders) to connect usage trends with financial outcomes
- Support pricing decisions for:
+ Practice Management subscriptions
+ Patient Solution modules (payments, patient engagement, claims)
- Partner with Commercial, Product and Customer Success to measure win/loss, attachment, NPS and overall customer sentiment
Partner with Commercial and Sales Operations to improve process over the way customer activity is classified (i.e. churn reason, consolidation relationship, DSO affiliation etc) and how included in customer count
*
*Data \& Systems Integration*
- Integrate and analyze data from SAP, Salesforce, Power BI and vendor reporting
Improve and standardize template KPI definitions, metric consistency, and data governance across Finance and Operations
*
*Executive Reporting \& Strategic Support*
- Prepare dashboards and board\-level materials focused on KPIs
- Deliver insights to leadership on ARR changes, customer activity, customer value, customer cohorts and growth initiatives
Support ad\-hoc analysis for new module launches, acquisitions, or enterprise DSO deals
*
KEY/CRITICAL COMPETENCIES:
- Strong analytical and problem\-solving skills
- Strong communication and cross\-functional collaboration skills
- Curiosity and enthusiasm for AI\-enabled finance
- Ability to translate complex data into executive\-ready insights
- Comfort working in a fast\-growing, product\-driven SaaS environment
High attention to detail and data integrity
*
REQUIRED SKILLS/PREFERRED SKILLS:
- 3\-5 years of experience in FP\&A or financial analysis, preferably in SaaS, healthcare IT, or practice management software
- Strong financial modeling and advanced MS Excel skills Experience with Power BI or similar BI tools
- Experience using and implementing AI tools or automation in a finance or analytics context
- Experience supporting DSO or multi\-location healthcare customers preferred
Experience with SaaS metrics and usage\-based or hybrid SaaS pricing models preferred
*
EDUCATION (Required/Preferred):
Bachelors degree in Finance, Accounting, Economics, Data Analytics, or related field
*
Salary: $80,000 \- $100,000 USD, based on experience.
Salary Context
This $80K-$100K range is in the lower quartile 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 CareStream Dental, 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($90K) sits 59% below the category median. Disclosed range: $80K to $100K.
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.
CareStream Dental AI Hiring
CareStream Dental has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $100K - $100K.
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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