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About This Role
Company: White Shore Funding Location: New York, NY (In Person) Salary: Up to $80,000 per year Job Type: Full-Time
Job Overview
White Shore Funding is seeking an exceptional Financial Analyst with advanced expertise in artificial intelligence, programming, financial analysis, and business automation.
This is not a traditional financial analyst position. We need someone who combines exceptional financial judgment with strong technical development skills to build AI-powered systems, automate company processes, improve financial reporting, and help scale our operational infrastructure.
The ideal candidate is highly analytical, technologically advanced, extremely efficient, and capable of independently developing practical solutions to complex business problems.
Key ResponsibilitiesAI and Business Automation
- Design, develop, and implement AI systems that automate financial and operational processes.
- Build and manage AI agents, custom chatbots, and intelligent automation workflows.
- Use ChatGPT, Grok, and other leading AI platforms at an expert level.
- Develop AI-powered tools for financial underwriting, cash-flow analysis, reporting, compliance monitoring, and business intelligence.
- Integrate AI systems with business software, databases, spreadsheets, and internal applications.
- Identify repetitive company tasks and develop systems that reduce or eliminate manual work.
- Build tools that extract, interpret, and analyze information from bank statements, financial documents, emails, and spreadsheets.
- Research emerging AI technologies and independently implement productivity improvements.
- Evaluate AI systems for accuracy, reliability, security, and compliance.
Programming and Technical Development
- Write and maintain code that automates financial and business operations.
- Use Python, SQL, VBA, and related technologies to develop practical internal solutions.
- Build API integrations connecting AI models with internal and third-party software.
- Create automated dashboards, internal applications, reporting systems, and data-processing tools.
- Develop workflows using Microsoft Power Automate, Zapier, Make, or similar platforms.
- Independently troubleshoot technical issues and implement reliable solutions.
- Use AI-assisted coding tools to accelerate development while maintaining accuracy.
Advanced Excel and Microsoft 365
- Build complex financial models using advanced formulas, XLOOKUP, INDEX/MATCH, pivot tables, Power Query, Power Pivot, macros, and VBA.
- Automate spreadsheets, financial calculations, reconciliations, and recurring reports.
- Create financial dashboards and interactive business-performance reports.
- Demonstrate expert-level proficiency with Microsoft Excel and Microsoft 365.
- Use AI within Microsoft applications to improve workflow efficiency.
- Process large volumes of financial data quickly and accurately.
Financial Analysis and Reporting
- Perform detailed cash-flow analysis to assess business financial health and repayment capacity.
- Analyze bank statements, balance sheets, profit-and-loss statements, expenses, and financial trends.
- Develop forecasts, budgets, cash-flow projections, and profitability models.
- Identify financial inconsistencies, unusual transactions, and potential risks.
- Support financial reporting, reconciliations, and performance tracking.
- Evaluate business models, revenue streams, operating expenses, and overall profitability.
- Use AI-driven analysis to improve financial decision-making.
- Develop financial models that help management evaluate operational efficiency and growth opportunities.
Compliance, Risk Management, and Internal Controls
- Maintain an understanding of financial regulations, business-lending requirements, and risk management.
- Develop automated systems that identify potential compliance violations, financial discrepancies, and operational risks.
- Implement internal controls, documentation procedures, and financial-monitoring systems.
- Support financial audits, regulatory reporting, and risk assessments.
- Protect sensitive financial information through secure technology and responsible AI implementation.
Required QualificationsFinancial Analysis
- Strong professional experience in financial analysis, cash-flow analysis, and financial modeling.
- Exceptional mathematical ability and financial judgment.
- Deep understanding of revenue, expenses, cash flow, profitability, and business operations.
- Experience analyzing bank statements and business financial statements.
- Knowledge of financial compliance, risk assessment, and internal controls.
- Ability to interpret complex financial information and provide actionable recommendations.
AI and Programming
- Expert-level proficiency with ChatGPT, Grok, and multiple AI platforms.
- Demonstrated experience building AI agents, chatbots, and automated workflows.
- Strong coding skills with the ability to independently develop working programs and automations.
- Experience with APIs, AI integrations, and large-scale data processing.
- Proven ability to use AI to solve complex business problems.
- Ability to evaluate AI-generated output and identify inaccuracies.
Technology and Productivity
- Expert-level Microsoft Excel skills.
- Advanced proficiency across Microsoft Office and other business software.
- Fast and accurate typing and data-processing skills.
- Exceptional computer literacy and technical problem-solving ability.
- Ability to work efficiently across multiple systems without sacrificing accuracy.
- Ability to independently learn new software and emerging technologies.
Ideal Candidate
We are looking for someone who is:
- Extremely sharp with numbers and able to identify financial errors quickly.
- An AI expert who builds functional systems rather than simply writing prompts.
- A strong programmer capable of independently developing tools and integrating software.
- Fast, accurate, and comfortable processing large amounts of information.
- Highly analytical and capable of making sound financial recommendations.
- Business-minded, with a practical understanding of profitability and operational efficiency.
- Independent and resourceful when solving technical or operational problems.
- Constantly searching for ways to reduce expenses, eliminate unnecessary work, and increase productivity.
- Detail-oriented and capable of maintaining accuracy while handling complex responsibilities.
Candidate Skills Assessment
Qualified applicants should be prepared to complete practical assessments involving:
- Advanced Excel financial modeling and automation.
- Cash-flow analysis and financial-statement interpretation.
- AI-agent development and workflow automation.
- Python programming or a comparable coding exercise.
- Practical use of ChatGPT, Grok, and AI-assisted business tools.
- Financial compliance and analytical problem-solving.
- Typing speed, accuracy, and general technical proficiency.
Why Join White Shore Funding?
This is an opportunity to play a critical role in developing the technological and financial infrastructure of a growing financial-services company.
You will work on real-world projects combining artificial intelligence, financial analysis, business strategy, programming, and automation.
Our objective is to automate as much of our financial and operational infrastructure as possible while maintaining exceptional accuracy, compliance, and financial oversight.
We are looking for someone who can do more than analyze financial information. We want someone capable of building the AI-powered financial systems that help operate and scale an entire company.
If you possess exceptional financial intelligence, advanced technical abilities, deep AI expertise, and a drive to improve how businesses operate, we encourage you to apply.
Pay: Up to $80,000 per year Work Location: In person — New York, NY Job Type: Full-Time
Pay: Up to $80,000.00 per year
Work Location: In person
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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Whiteshore Funding LLC, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Whiteshore Funding LLC AI Hiring
Whiteshore Funding LLC has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span New York, NY, US, Lexington, KY, US.
Location Context
AI roles in New York pay a median of $221,000 across 3,257 tracked positions. That's 4% above 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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