Interested in this Data Scientist role at Deloitte?
Apply Now →Skills & Technologies
About This Role
Our Global Investment and Innovation Incentives ("Gi3") practice provides our clients a broad range of government credits and incentives ("C\&I") services across more than 150 countries and regions worldwide. With over 1,000 practitioners, the Gi3 tax team is a national team of specialists dedicated to providing comprehensive tax C\&I services.
If you are a technology visionary with a passion for transforming global tax business with digital technology, consider working with the Gi3 technology team. This is an exciting opportunity to support global execution of Deloitte's tax strategy as we shift from "doing digital" to "being digital" by reimagining how we engage with our clients, deliver our services, operate our business, and create value.
Work you'll do
We are seeking a Data Scientist with strong Generative AI (GenAI) and Natural Language Processing (NLP) skill to build and optimize LLM\-powered prototypes and production\-ready components. You will design prompts and Retrieval\-Augmented Generation (RAG) workflows, package models as APIs, and support cloud deployment while ensuring outputs are evaluated, monitored, and aligned to responsible AI expectations. This role requires hand\-on Python development, solid statistical modeling fundamentals, and the ability to communicate clearly across technical and non\-technical stakehold ers.
- Application Development: Build and optimize GenAI/NLP models and prototypes using modern framework e.g., OpenAI, Hugging Face).
- Prompt Engineering: Design, test and refine prompts to improve quality and reduce risk (e.g., hallucinations, bias, unsafe outputs).
- RAG Workflows: Implement foundational Retrieval\-Augmented Generation pipelines to enable context\-aware applications.
- APIs and Cloud Development: Package models as APIs and support deployments on AWS (Amazon Web Services, Azure, or GCP (Google Cloud Platforms).
- Validation and Monitoring: Develop evaluation approaches and monitor model outputs for reliability, performance drift, and compliance needs.
Documentation and Communication: Produce clear and technical documentation and explain designs, tradeoffs, and results to cross\-functional partners.
*
Qualifications
Required:
- Ability to perform job responsibilities within a hybrid work model that requires US Tax Processionals to co\-locate in person 2\-3 days per week
- Limited immigration sponsorship may be available
- Ability to travel 25% on average, based on the work you do and the clients and industries/sectors you serve.
- Bachelor's Degree in Information Systems, Computer Science or related field required.
- 3\+ years of experience in data science / machine learning
- 1\+ year hands\-on building LLM/NLP solutions
- Strong Python proficiency and hands\-on experience with GenAI frameworks such as LangChain, OpenAI/GPT APIs and Hugging Face
- Familiarity with software engineering best practices such as Git, CI/CD, automated testing.
- Experience with R, Python, SQL and basic cloud ML deployment on Azure, AWS or GCP.
- One of the following active accreditations obtained, in process, or willing and able to obtain:
- + Licensed CPA in state of practice/primary office if eligible to sit for the CPA exam
+ If not CPA eligible, one of the following:
+ - Licensed Attorney
- Enrolled Agent
- Certifications:
- * Professional Engineer
- Project Management Professional (PMP)
- Chartered Financial Advisor (CFA)
- Technology Certifications:
- * CBAP \- Certified Business Analysis Professional
- Certified SAFe Lean Portfolio Manager
- Certified SAFe Architect
- Certified SAFe Agile Software Engineer
- Certified SAFe Proudct Owner / Product Manager
- Certified SAFe Agilist
- Certified SAFe Advanced Scrum Master Professional Scrum Developer™ (PSD)
- Certified SAFe® Scrum Master
- Certified SAFe® DevOps Practitioner
- Certified SAFe® Practioner
- Microsoft Certified Solutions Developer (MCSD)
- Microsoft Certified Solutions Expert (MCSE)
- Professional Scrum Product Owner (PSCPO) \- SCRUM.org and Project Management Professional (PMP
- Six Sigma (Green or Black Belt)
- ITIL Certification:
- * Other: Vendor certification for management of implementations (Oracle, SAP, Thomson Reuters, etc.) or relevant industry certification such as Microsoft Certified Solutions Developer (MCSD), AWS (Amazon Web Services) or GCP (Google Cloud Platform)
Preferred:
- Experience fine\-tuning LLMs and/or building conversational AI agents.
- Awareness of Responsible AI, privacy, security, and AI ethics considerations
- Exposure to data engineering and/or MLOps (Machine Learning Operations) practices.
- Excellent written and communication skills.
Strong problem solving skills
*
The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $84,600 to $141,000\.
You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.
nftstax
Salary Context
This $84K-$141K range is in the lower quartile for Data Scientist roles in our dataset (median: $155K across 226 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 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Deloitte, 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 463 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($112K) sits 42% below the category median. Disclosed range: $84K to $141K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Deloitte AI Hiring
Deloitte has 15 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Washington, DC, US, Fort Worth, TX, US, McLean, VA, US. Compensation range: $141K - $338K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.