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About This Role
Senior Analyst, Data \& Analytics (AI)
About Palm Tree
Palm Tree is a modern M\&A value creation firm that integrates financial consulting, operational consulting, and investment banking services. Founded in 2010, Palm Tree partners with private equity firms, business operators, and management teams through strategic events including acquisitions, carve\-outs, recapitalizations, restructurings, integrations, and performance improvement initiatives.
Palm Tree’s Data \& Analytics practice helps private equity portfolio companies and middle\-market businesses build modern, insight\-driven data capabilities. Our team works alongside executive leadership to design, implement, and optimize data infrastructure and analytics solutions that provide real\-time visibility into operational and financial performance.
The Senior Analyst Position
The Senior Analyst is a practitioner\-level contributor within Palm Tree's Data \& Analytics practice. Senior Analysts build and deploy analytics and machine learning solutions across active client engagements, driving measurable operational and financial outcomes for PE\-backed portfolio companies.
This role blends hands\-on technical development with applied AI/ML work. Senior Analysts design and implement predictive models, automate analytical workflows, and develop scalable data infrastructure that powers real\-time reporting and advanced analytics. The role is embedded in client delivery and requires direct engagement with business stakeholders.
The ideal candidate has a strong foundation in data engineering and BI, a working command of machine learning methodologies, and the ability to translate analytical outputs into clear business recommendations. Experience in consulting, PE, or fast\-paced operational environments is a strong plus.
Core Responsibilities
AI/ML Model Development \& Deployment* Build and deploy supervised and unsupervised ML models including forecasting (ARIMA, VAR, gradient boosting), classification, clustering, and anomaly detection across client datasets
- Design and implement NLP pipelines for topic modeling, text classification, and unstructured data analysis on operational and financial records
- Develop and maintain experimentation frameworks including A/B testing, lift analysis, and causal inference to evaluate operational interventions
- Translate model outputs into actionable business recommendations for operations, finance, and commercial leadership teams
- Apply imbalance correction, cross\-validation, and threshold optimization to ensure model reliability in production environments
Data Engineering \& Pipeline Development* Design and build scalable ETL/ELT pipelines using Azure Data Factory, Python, and cloud\-native tools to ingest data from ERP, CRM, and operational source systems
- Write production\-grade SQL to model, transform, and validate data across complex multi\-table schemas
- Architect cloud data infrastructure on Azure (Azure SQL, ADLS, ADF) and Snowflake to support analytics and ML workloads
- Reduce data latency and improve pipeline reliability through automated orchestration and monitoring
Business Intelligence \& Reporting* Develop and maintain Power BI semantic models, DAX measures, and executive dashboards that surface operational and financial KPIs
- Design KPI reporting frameworks providing leadership teams with real\-time visibility into performance across inventory, revenue, and operations
- Implement row\-level security, governance controls, and data quality checks within BI environments
Client Engagement \& Analytics Strategy* Partner directly with portfolio company stakeholders to define analytical requirements, success metrics, and delivery frameworks
- Translate complex business questions into data models, ML problem statements, and analytical frameworks
- Lead working sessions with business and technical stakeholders to align on priorities and communicate findings clearly
- Independently manage analytics workstreams including scoping, effort estimation, and delivery execution
Team Development \& Practice Growth* Mentor and support junior analysts across client engagements
- Contribute to internal knowledge development including ML frameworks, data architecture standards, and reusable analytics assets
- Support development of AI/ML methodologies and delivery accelerators within the practice
Qualifications* 3\-5\+ years of experience in data analytics, data science, or data engineering roles
- Proficiency in Python for ML model development (scikit\-learn, statsmodels, NumPy, Pandas) and data pipeline automation
- Strong SQL proficiency including CTEs, window functions, subqueries, and complex joins across large\-scale schemas
- Hands\-on experience building and deploying machine learning models in production or near\-production environments
- Experience with forecasting methodologies (ARIMA, VAR, gradient boosting) and/or NLP techniques (LDA, text classification)
- Demonstrated ability to design and evaluate controlled experiments including A/B testing and lift analysis
- Experience building Power BI models, DAX measures, and dashboards for operational or financial reporting
- Strong communication skills with the ability to translate technical findings to non\-technical business audiences
- Ability to independently manage multiple workstreams in fast\-paced consulting or advisory environments
Preferred Experience* Experience working within private equity portfolio company environments or management consulting
- Hands\-on experience with Azure Data Factory, Azure SQL, ADLS, or Snowflake for cloud data infrastructure
- Exposure to ERP systems such as AS/400, NetSuite, QuickBooks, or similar platforms
- Familiarity with BI tools such as Tableau, Power BI, or Databricks for analytical delivery
- Experience with R for statistical modeling or time\-series analysis
- Graduate degree in Analytics, Statistics, Computer Science, Economics, or a related quantitative field
Compensation and Benefits* Base salary range of $100,000\-$125,000, with performance\-based bonus opportunities
- Comprehensive benefits package including medical, dental, and vision insurance
- Competitive 401(k) program with employer matching contributions
- Hybrid work environment with access to offices in Los Angeles, Chicago, and New York
- Unlimited paid time off (PTO)
- Opportunities for career advancement within a merit\-based, entrepreneurial culture
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What to Expect When Applying
Whether this is your first time applying to Palm Tree or you’ve connected with us before, you are welcome to use this portal to submit an updated résumé, cover letter, or other materials. Our recruiting team will thoughtfully incorporate your most recent information into your existing application profile. And yes—we do read cover letters. In fact, *the cover letter is a great place to demonstrate why you're qualified for a position, even if you do not meet all requirements.*
Application Confirmation
After submitting your application, you will receive an email confirming that we’ve received your materials.
Initial Review
Once your application has been reviewed and added to our active candidate database, you will receive a second confirmation email. Timing for this step can vary based on current hiring needs and priorities, so we appreciate your patience.
*Note: If a role is marked as an “immediate” opening, the posting will typically outline specific timelines for each stage of the process, including a potential start date.*
Screening
If and when a suitable opportunity arises, a member of our hiring team will reach out to schedule an introductory conversation. The timing of this step is demand\-driven and can vary.
Interviews
Qualified candidates will be invited into a formal interview process. As with earlier stages, timing may vary depending on hiring priorities and role requirements.
While we do carefully review every application, we are unable to provide individual feedback to all candidates due to application volume. We value transparency and sincerely appreciate your understanding.
*Important Notice*
Palm Tree is not currently engaging outside recruiters or headhunters for this search. Unsolicited résumé submissions from third parties may inadvertently disqualify a candidate. All interested and qualified candidates should apply directly through this portal.
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Salary Context
This $100K-$125K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Palm Tree, 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($112K) sits 37% below the category median. Disclosed range: $100K to $125K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Palm Tree AI Hiring
Palm Tree has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Los Angeles, CA, US. Compensation range: $125K - $125K.
Location Context
AI roles in Los Angeles pay a median of $189,000 across 1,686 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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