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About This Role
Vice President, Data & Analytics
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, AI solutions, and analytics that provide real-time visibility into operational and financial performance.
The Vice President Position
The Vice President is a senior management role within Palm Tree's Data & Analytics practice and a hands-on execution leader. This role manages analytics engagements and the teams that deliver them, designs data models and architecture that other team members build on, develops advanced AI solutions, and strengthens the practice's delivery quality and capacity.
The VP works directly with private equity sponsors, operators, and executive stakeholders to translate financial and operational questions into scalable data and AI solutions. The role combines deep technical capability in business intelligence, data modeling, and AI with a firm foundation in finance and accounting. The ideal candidate speaks the financial language expected of private equity operators, can analyze financial statements and forecasts as well as build the solutions that support them, receives direction while independently driving execution, and helps build the team's capabilities over time.
Core Responsibilities
Data Architecture & Modeling
- Design scalable, interconnected data models (including dimensional and relational structures) that go well beyond single-table solutions
- Establish database-style design standards, modeling conventions, and analytics architecture best practices across engagements
- Serve as a second technical reviewer for data models and transformation logic, ensuring quality and consistency across project delivery
- Document data structures, business rules, and pipeline logic for client and internal use
Financial Data & Accounting Foundation
- Model, validate, and reconcile financial data from ERP and accounting systems, including income statements, balance sheets, and cash flow statements
- Build and maintain forecasting, budgeting, and budget-versus-actual models that reflect how finance teams plan and track performance
- Define financial metrics precisely, including EBITDA, working capital, net debt, margins, and covenant calculations, so that data outputs align with the accounting record
- Support month-end close, reporting, and financial data quality processes in partnership with client finance teams
Business Intelligence & Financial Analytics
- Lead the development of Power BI semantic models, reports, and dashboards using Power Query, DAX, and sound data modeling practices
- Build finance-informed KPI frameworks that connect financial requirements to technical solutions
- Apply advanced Excel-based analysis to validate results, support financial modeling, and communicate with finance stakeholders
- Implement row-level security and governance controls within BI environments, and support data quality assurance
Financial Analysis & Advisory
- Analyze historical performance, forecasts, and variances to identify the drivers behind results and inform operational and financial decisions
- Interpret financial statements and forecasts for private equity sponsors, operators, and management teams, translating findings into clear recommendations
- Prepare board, lender, and investment committee reporting materials that present financial performance and value creation progress
AI Solutions Development
- Lead the development of AI agents, custom GPTs, and Copilot-based solutions that support analytics delivery and firm-wide initiatives
- Provide architectural guidance to ensure AI solutions are scalable, efficient, secure, and integrated with data models and BI environments
- Identify and implement AI-related tools and workflows that accelerate analysis, forecasting, modeling, and reporting
- Maintain and optimize the firm's AI agent library so that it remains current and relevant
- Evaluate where AI capabilities add practical value to client deliverables and internal processes
AI Enablement, Training & Collaboration
- Train internal and external users on the development and use of AI tools, including Copilot
- Work closely with third-party vendors to onboard and train users on AI technologies
- Collaborate with departments across the firm and with clients to identify AI-driven opportunities and implement tailored solutions
- Stay current on AI technologies, trends, and best practices, and act as a thought leader driving AI adoption across business units
Client & Stakeholder Engagement
- Work directly with executive and business stakeholders to translate business questions into analytical and AI-enabled frameworks
- Lead working sessions, status meetings, and presentations with business and technical stakeholders
- Partner with clients to define metrics, reporting needs, and data requirements that support operational and financial decisions
Delivery & Engagement Management
- Own complex analytics and AI workstreams from scoping through delivery, including timelines, resourcing, and quality
- Scope projects, estimate level of effort, and manage delivery timelines across one or more teams
- Manage budget, revenue, and gross margin for assigned engagements, in line with firm performance goals
- Synthesize insights from structured and unstructured data and deliver actionable recommendations that support operational and financial decision-making
Team Leadership & Development
- Lead and support junior team members across analytics and AI engagements, helping balance workload and project volume
- Provide structured feedback and guidance on modeling, analytics architecture, financial analysis, and best practices
- Mentor and develop team members, contributing to training efforts and succession planning
- Reduce the practice's dependence on any single technical reviewer for models and solution quality
- Contribute to internal methodologies, delivery frameworks, and practice standards, and support firm initiatives and a collaborative culture
Qualifications
Education & Experience
- Bachelor's degree in accounting, finance, business, or a similar field
- 7+ years of relevant professional experience in financial transaction advisory, private equity, consulting, investment banking, restructuring, corporate or portfolio company finance, FP&A, or business intelligence, with progressive responsibility
- Proven ability to lead junior staff and produce quality client results
- Prior experience managing, leading, or supporting a team
Finance & Accounting
- Strong foundation in finance and accounting, with fluency in financial statements, forecasts, budgets, and the financial language used by private equity operators
- Ability to analyze financial statements and forecasts, identify performance drivers, and communicate conclusions to sponsors and management
- Understanding of accounting principles and close processes, and the ability to work with financial data at the detail level required for accurate reporting
- Experience combining data and analytics skills with finance-related knowledge; finance or accounting background highly desirable
- Familiarity with finance concepts and business processes, with the ability to bridge financial requirements and technical solutions
Data, BI & Technical
- Strong BI background with hands-on expertise in Power BI, Power Query, DAX, and data modeling
- Demonstrated experience building scalable, multi-table data structures and interconnected models, with a working understanding of database design principles
- Strong SQL proficiency, including CTEs, window functions, subqueries, and complex joins
- Advanced proficiency in Microsoft Excel and PowerPoint
AI
- Demonstrated experience developing AI solutions, including AI agents, custom GPTs, or Copilot-based tools
- Experience with AI-related tools and workflows, and the judgment to determine where AI adds practical value
Professional Attributes
- Experience working directly with private equity sponsors, business stakeholders, and senior leaders in client-facing environments
- Strong analytical thinking and problem decomposition skills, with the ability to work independently, receive direction, and iterate on solutions with limited supervision
- Initiative-taking, motivated, and self-starter attitude
- Meticulous attention to detail and the ability to thrive under pressure in fast-paced settings
- Excellent written, verbal, and interpersonal communication skills, with the ability to engage decision makers across functions
- Demonstrated business-partner mindset
Preferred Experience
- CPA or CFA designation
- Experience with cloud data platforms such as Azure or Snowflake
- Familiarity with ETL/ELT tools including Azure Data Factory or dbt
- Experience using Python for data transformation or automation
- Exposure to private equity or portfolio company environments
- Familiarity with ERP systems such as AS/400, NetSuite, or similar
Compensation and Benefits
- Base salary range of $150,000 - $200,000, with competitive performance-based bonus incentive
- 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 $150K-$200K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. This role's midpoint ($175K) sits 17% below the category median. Disclosed range: $150K to $200K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
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: $200K - $200K.
Location Context
AI roles in Los Angeles pay a median of $210,000 across 1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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