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About This Role
Location: New York
Other locations: Anywhere in Region
Salary: Competitive
Date: Aug 11, 2026
Job description
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Requisition ID: 1725693
Location: New York, Dallas
At EY, we’re all in to shape your future with confidence.
We’ll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go. Join EY and help to build a better working world.
Will you shape the future or will the future shape you?
The opportunity
Tax Technology and Transformation provides services to help companies address the challenges posed by existing and emerging technologies, particularly the increasing data burden, by enhancing efficiency and transforming tax functions into cost\-effective operations. The aim is to assist businesses in navigating the digital landscape of tax transparency, compliance, and audit methods while tackling pressing challenges. Key offerings include:
- digital tax transformation,
- tax applications\-as\-a\-service,
- tax data improvement,
- analytics enhancement, and
- emerging technologies like robotic process automation (RPA), artificial intelligence (AI), and blockchain.
Additionally, services encompass tax technology program mobilization, custom application development, strategy road mapping, implementation of direct and indirect tax systems, post\-transaction operational services, and assessments of tax functions. In today's evolving corporate environment, implementing a transaction tax calculation engine is crucial for automating tax determination and compliance processes across various transactional systems, including ERP applications and custom solutions.
Key responsibilities
We are looking for an ambitious, self\-motivated data scientist who will help us discover the information hidden in vast amounts of data and help us deliver even better products to our clients. Your primary focus will be in applying data mining techniques, doing statistical analysis and building high quality prediction systems integrated with our products. You will be expected to team on a national and even global scale, so strong communication skills, attention to detail, and ability to effectively drive results are essential.
- Selecting features and building and optimizing classifiers using machine learning techniques
- Large Language Models (LLM) prompting and deployment techniques
- Designing and building AI Agents to automate and enhance complex business tasks
- Integration of AI components into full\-scale solutions to support business processes
- Data mining using state\-of\-the\-art methods
- Enhancing data collection procedures to include information that is relevant for building analytic systems
- Processing, cleansing and verifying the integrity of data used for analysis
- Doing ad\-hoc analysis and presenting results in a clear manner
Depending on your unique skills and ambitions, you could be supporting various client projects, ranging from assisting in the production of leading\-edge machine learning models, to designing and implementing robust data pipelines that can handle data at a multinational, enterprise scale. Whatever you find yourself doing, you will contribute and help toward developing a highly trained team, all the while handling activities with a focus on quality and commercial value. This is a highly regulated industry, so it is all about maintaining our reputation as trusted advisors by taking on bold initiatives and owning new challenges.
Skills and attributes for success
To qualify for the role, you must have
- A bachelor's degree in data science, information system, tax technology, management information systems or computer science or related field and a minimum of one year of related work experience
- Approved technical certification, CPA license or membership to state Bar or progress to attaining those mentioned
- A passionate interest in data science and its role in organization
- Excellent communication and business writing skills
- A natural flair for problem solving and an entrepreneurial approach to work
- Strong organizational and time management skills, with exceptional client\-serving consulting skills
- Desire and demonstrated ability to provide leadership within a team
- Willingness to travel as needed, and working in a balanced hybrid environment
- Strong understanding of machine learning techniques and algorithms, such as Linear/Logistic Regression, k\-NN, Naïve Bayes, Support Vector Machines (SVM), Random Forests, etc.
- Strong command and up\-to\-date knowledge of the latest developments in the field of Artificial Intelligence, including but not limited to Generative AI, and AI Agents.
- Hands\-on experience with LLMs, and AI Agents
- Strong working knowledge and at least 1\-2 years of experience with Python
- Strong knowledge and experience using the Python toolkit (Pandas, NumPy, Jupyter Notebooks, etc.) are essential
- Experience with data visualization tools, such as PowerBI, Seaborn, Matplotlib, etc.
- Experience with one of the following: SQL and NoSQL database technologies, SQL Server, MongoDB, Databricks, or Snowflakes
- Strong scripting and programming skills. Ability to write clean code and understand Object Oriented Programming concepts.
- Ownership of assigned tasks and monitoring them until completion, including documenting requirements, configuration, testing and debugging.
- Ability to identify ways to automate manual tasks using existing financial or tax systems and emerging technologies
- Ability to consolidate data to make analysis and planning more efficient
- Focus on improving reporting capabilities to enhance our clients' ability to evaluate risk and capitalize on opportunities
- Willingness to support project team members in any way needed to help ensure timely completion of deliverables
Ideally, you will have
- Experience with Apache Spark, Databricks, Snowflakes or other data tools
- Experience working in the Microsoft Azure Cloud environment, AWS, GPT or other Cloud environment
- Experience developing ETL solutions using SSIS or other tools
- ERP experience, including SAP and/or Oracle\-preferred but not required
- Practical experience or strong theoretical understanding of neural networks and their applications
What we look for
We are looking for knowledgeable data science professionals with a passion for turning data into actionable insight. You will need strong business acumen and a firm strategic vision, so if you are ready to use those skills to develop your team, this role is for you.
Are you ready to shape your future with confidence? Apply today.
What we offer you
At EY, we’ll develop you with future\-focused skills and equip you with world\-class experiences. We’ll empower you in a flexible environment, and fuel you and your extraordinary talents in a diverse and inclusive culture of globally connected teams. Learn more.
- We offer a comprehensive compensation and benefits package where you’ll be rewarded based on your performance and recognized for the value you bring to the business. The base salary range for this job in all geographic locations in the US is $76,600 to $126,300\. The base salary range for New York City Metro Area, Washington State and California (excluding Sacramento) is $91,800 to $143,400\. Individual salaries within those ranges are determined through a wide variety of factors including but not limited to education, experience, knowledge, skills and geography. In addition, our Total Rewards package includes medical and dental coverage, pension and 401(k) plans, and a wide range of paid time off options.
- Join us in our team\-led and leader\-enabled hybrid model. Our expectation is for most people in external, client serving roles to work together in person 40\-60% of the time over the course of an engagement, project or year.
- Under our flexible vacation policy, you’ll decide how much vacation time you need based on your own personal circumstances. You’ll also be granted time off for designated EY Paid Holidays, Winter/Summer breaks, Personal/Family Care, and other leaves of absence when needed to support your physical, financial, and emotional well\-being.
Are you ready to shape your future with confidence? Apply today.
EY accepts applications for this position on an on\-going basis.
For those living in California, please click here for additional information.
EY focuses on high\-ethical standards and integrity among its employees and expects all candidates to demonstrate these qualities.
EY \| Building a better working world
EY is building a better working world by creating new value for clients, people, society and the planet, while building trust in capital markets.
Enabled by data, AI and advanced technology, EY teams help clients shape the future with confidence and develop answers for the most pressing issues of today and tomorrow.
EY teams work across a full spectrum of services in assurance, consulting, tax, strategy and transactions. Fueled by sector insights, a globally connected, multi\-disciplinary network and diverse ecosystem partners, EY teams can provide services in more than 150 countries and territories.
EY provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, genetic information, national origin, protected veteran status, disability status, or any other legally protected basis, including arrest and conviction records, in accordance with applicable law.
EY is committed to providing reasonable accommodation to qualified individuals with disabilities including veterans with disabilities. If you have a disability and either need assistance applying online or need to request an accommodation during any part of the application process, please call 1\-800\-EY\-HELP3, select Option 2 for candidate related inquiries, then select Option 1 for candidate queries and finally select Option 2 for candidates with an inquiry which will route you to EY’s Talent Shared Services Team (TSS) or email the TSS at [email protected].
Salary Context
This $76K-$143K range is in the lower quartile for Data Scientist roles in our dataset (median: $160K across 258 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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At EY, 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 $192,890 based on 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($110K) sits 43% below the category median. Disclosed range: $76K to $143K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
EY AI Hiring
EY has 17 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Software Engineer, Data Engineer. Positions span Chicago, IL, US, New York, NY, US, Hoboken, NJ, US. Compensation range: $142K - $390K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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