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About This Role
Location
United States
Start and end date
22 Oct 2026 – 21 Oct 2027 Indicative hourly rate
$100 – 105/hr
Respond by
10 Oct 2026
Skills
Search, Knowledge Management, Python programming, Machine learning, Language models, Renewable energy, Anomaly detection, Data Science, Energy storage systems, retrieval, Artificial Intelligence, Data analysis, Model, Time series, Analytical, Application programming interfaceSearch, Knowledge Management, Python programming, Machine learning, Language models, Renewable energy, Anomaly detection, Data Science, Energy storage systems, retrieval, Artificial Intelligence, Data analysis, Model, Time series, Analytical, Application programming interface View all
Role Summary / Purpose
We are seeking an Applied AI/ML Engineer / Data Scientist to develop and operationalize the intelligence layer of NextBrain. This role will combine data science, machine learning, generative AI and software engineering to create intelligent capabilities that help users understand operational information, diagnose issues, identify anomalies, retrieve knowledge and make better decisions. Unlike a traditional research-focused Data Scientist position, this role emphasizes taking AI capabilities from experimentation through production deployment and evaluation.
Key Responsibilities
- Design, develop, evaluate and deploy AI/ML capabilities within NextBrain.
- Develop analytical models for anomaly detection, asset health, forecasting, classification and other operational use cases.
- Develop generative AI and agentic capabilities using enterprise-approved foundation models and AI platforms.
- Design prompts, tools, agents, workflows and orchestration patterns for NextBrain.
- Develop Retrieval-Augmented Generation and knowledge-retrieval solutions when appropriate.
- Create rigorous evaluation frameworks for LLM and agent behavior.
- Establish metrics for model accuracy, relevance, reliability, hallucination, latency and cost.
- Develop guardrails and validation mechanisms for AI-generated responses.
- Collaborate with Data Engineering to define training, inference, retrieval and feature-data requirements.
- Collaborate with the Full Stack/Cloud Engineer to deploy AI services into production.
- Develop prototypes rapidly while designing solutions that can transition into production.
- Monitor model and agent performance and continuously improve deployed capabilities.
- Communicate model behavior and analytical findings to engineers, product stakeholders and operational subject-matter experts.
- Stay current with emerging AI, agentic AI, ML and data-science technologies and assess their applicability to NextBrain.
Requirements – Education & Experience
- Master's degree or Bachelor's degree with equivalent experience in Computer Science, Data Science, Engineering, Statistics, Machine Learning or a related discipline.
- 4+ years of experience developing machine-learning or advanced analytics solutions.
- Strong Python skills.
- Experience with common ML/data-science frameworks and libraries.
- Experience taking analytical or ML solutions from experimentation into production.
- Strong foundation in statistics, experimentation, model evaluation and data analysis.
- Experience working with cloud-based data and compute environments.
- Experience with APIs, software-development practices, source control and CI/CD.
- Demonstrated ability to translate business or operational problems into analytical approaches.
Nice-to-Have / Preferred Skills
- Hands-on experience developing applications using LLMs.
- Experience with agentic frameworks, tool calling, MCP or similar AI orchestration technologies.
- Experience with RAG, embeddings, vector search and knowledge-management architectures.
- Experience implementing systematic LLM evaluation and guardrails.
- Experience with AWS AI/ML services.
- Experience with time-series analytics and anomaly detection.
- Experience with industrial, energy, renewable-generation, BESS or operational datasets.
- Familiarity with MLOps and model-monitoring practices.
Growth & Development Opportunities
NextBrain should evolve from simply providing access to applications and data toward becoming an intelligent operational assistant, while its AI outputs remain measurable, explainable where required, secure and trustworthy enough for enterprise operational use.
Requirements
Skills
- Search — Intermediate
- Knowledge Management — Intermediate
- Python programming — Intermediate
- Machine learning — Intermediate
- Language models — Intermediate
- Renewable energy — Intermediate
- Anomaly detection — Intermediate
- Data Science — Intermediate
- Energy storage systems — Intermediate
- retrieval — Intermediate
- Artificial Intelligence — Intermediate
- Data analysis — Intermediate
- Model — Intermediate
- Time series — Intermediate
- Analytical — Intermediate
- Application programming interface — Intermediate
Languages
- English — Professional working proficiency
Salary Context
This $208K-$218K range is above the 75th percentile for Data Scientist roles in our dataset (median: $167K across 150 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 8,943 AI roles we're tracking, Data Scientist positions make up 4% of the market. At vertage, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,406 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($213K) sits 12% above the category median. Disclosed range: $208K to $218K.
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.
vertage AI Hiring
vertage has 2 open AI roles right now. They're hiring across Data Scientist, AI Software Engineer. Based in US. Compensation range: $176K - $218K.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below the national median.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
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).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
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
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