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About This Role
Avicado Construction Technology Services is seeking a Data Science Consultant to lead the design and delivery of data visualization and analytics solutions across a portfolio of clients. This role blends hands\-on technical execution with client\-facing consulting, enabling organizations to unlock insights, improve decision\-making, and scale their data capabilities.
You will love this job if…
- You are a high performer, self\-starter, and love to learn.
- You take ownership of projects and drive impact.
- You enjoy working directly with clients and solving complex problems
- You thrive in fast\-paced, ambiguous environments
- You like to have fun and be your authentic self.
What you’ll do…
- Client Engagement \& Project Leadership
+ Lead Power BI and analytics engagements from requirements gathering through delivery and iteration
+ Serve as the primary point of contact for clients on reporting and analytics initiatives
+ Facilitate workshops to define KPIs, reporting needs, and business requirements
+ Manage timelines, deliverables, and expectations across multiple concurrent clients and projects
- Dashboard Design \& Development
+ Design and develop interactive Power BI dashboards and reports
+ Translate business requirements into intuitive visualizations and user experiences
+ Apply best practices in data storytelling, usability, and performance
- Data Modeling \& Transformation
+ Build and optimize data models using star schema and best practices
+ Develop DAX calculations, measures, and performance optimizations
+ Use Power Query to extract, transform, and structure data from multiple sources
- Technical Delivery \& Optimization
+ Establish efficient data refresh strategies and performance optimization techniques
+ Ensure solutions are scalable, maintainable, and aligned with enterprise standards
+ Troubleshoot and resolve data, performance, and visualization issues
- Team Collaboration \& Mentorship
+ Delegate and review work from junior analysts and developers
+ Provide mentorship and guidance to support team growth
+ Ensure consistency, quality, and best practices across deliverables
- Stakeholder Communication \& Enablement
+ Present dashboards, insights, and recommendations to stakeholders
+ Advise clients on data strategy, reporting improvements, and governance
+ Support adoption through training, documentation, and knowledge transfer
You should have...
- 5–8\+ years of experience in data analytics, BI development, or related roles
- 3–5\+ years of hands\-on experience with Power BI
- Proven experience leading end\-to\-end reporting or dashboard initiatives
- Advanced expertise in Power BI, data modeling, and visualization best practices
- Strong proficiency in DAX, Power Query (M), and data transformation techniques
- Experience working with multiple data sources (SQL, APIs, warehouses, Excel, etc.)
- Strong analytical thinking, problem\-solving, and communication skills
- Ability to manage multiple priorities in a fast\-paced environment
Preferred Qualifications
- Experience in consulting or client\-facing roles
- Familiarity with modern data platforms (Snowflake, Azure, Google BigQuery, etc.)
- Experience with ETL/ELT processes and data pipelines
- Experience with Power Platform tools (Power Automate, Power Apps)
- Experience with governance, deployment pipelines, and Power BI Service administration
- Experience supporting construction, capital projects, or data center environments
Characteristics of an ideal candidate
- Responsive; Avicado takes great pride in reacting quickly and positively to our clients and teammates, both internal and external
- Innovative; a desire to drive innovation through new and unique solutions while embracing creative ideas
- Entrepreneurial; the drive to take initiative, deliver results, and create value for our clients
- Empowered; bring solutions instead of problems
- Performance Driven \& Accountable; sets goals and challenges our high\-performance culture
- Even\-tempered; handles pressure and thrives in a fast\-paced environment
- Coachable; recognizes strengths \& weakness and open to guidance
Why Avicado
- Competitive compensation
- Health insurance
- 401k with employer match
- Flexible PTO
- Remote work
- Philanthropic Matching Gift Program And more…
About Avicado
Avicado, LLC was established in 2015 with a focus on utilizing the latest cloud\-based tools and applications to enhance our clients' experience. As a technology consultancy, we empower construction owners to make the most of their systems and data. Our team of experts collaborates with enterprise organizations such as data centers, universities, hospitals, and real estate developers to promote seamless interoperability across their teams and technologies. We are experiencing an exciting phase of expansion and actively searching for new talent to join our team.
We’re a close\-knit team with a high\-performance culture, but we don’t like to take ourselves too seriously.
Our diversity and inclusivity are a point of pride, and we have created a highly interactive remote work environment that encourages mutual respect and individuality while fostering opportunities for employees to excel both personally and professionally. We offer competitive benefits, remote work experiences, flexible work arrangements, various career development opportunities, employee resource groups, and more.
Avicado's unwavering dedication to creating a remarkable workplace experience has been widely acknowledged by experts in the industry. We are thrilled to have attained the highly coveted "Great Place to Work" certification and to have been included on Inc.'s esteemed Best Workplaces list for three consecutive years. Additionally, we are humbled to have received the AYA Award, which recognizes allies who promote equality and actively foster positive change for women in technology. At Avicado, we take great pride in fostering a culture that is both inclusive and supportive, especially for women in the technology industry.
These accolades are a testament to our ongoing efforts to foster a culture of inclusion, mutual respect, and professional growth for all members of our team.
If you are a self\-motivated individual who wants to work with Fortune 500 clients in a rapidly growing company, we encourage you to join us!
EOE
Transforming The Way Construction Owners Use Technology \& 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Avicado, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Avicado AI Hiring
Avicado 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 $170,000 across 1,926 positions. About 15% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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