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About This Role
At PwC, our people in data and analytics focus on leveraging data to drive insights and make informed business decisions. They utilise advanced analytics techniques to help clients optimise their operations and achieve their strategic goals.
In business intelligence at PwC, you will focus on leveraging data and analytics to provide strategic insights and drive informed decision\-making for clients. You will develop and implement innovative solutions to optimise business performance and enhance competitive advantage.
Driven by curiosity, you are a reliable, contributing member of a team. In our fast\-paced environment, you are expected to adapt to working with a variety of clients and team members, each presenting varying challenges and scope. Every experience is an opportunity to learn and grow. You are expected to take ownership and consistently deliver quality work that drives value for our clients and success as a team. As you navigate through the Firm, you build a brand for yourself, opening doors to more opportunities.
Examples of the skills, knowledge, and experiences you need to lead and deliver value at this level include but are not limited to:
Apply a learning mindset and take ownership for your own development.
Appreciate diverse perspectives, needs, and feelings of others.
Adopt habits to sustain high performance and develop your potential.
Actively listen, ask questions to check understanding, and clearly express ideas.
Seek, reflect, act on, and give feedback.
Gather information from a range of sources to analyse facts and discern patterns.
Commit to understanding how the business works and building commercial awareness.
Learn and apply professional and technical standards (e.g. refer to specific PwC tax and audit guidance), uphold the Firm's code of conduct and independence requirements.
The Opportunity
As part of the Financial Crime Unit team you will apply analytical methods to complex datasets leveraging SQL and Python to tackle financial crime challenges. As an Associate you will focus on learning and contributing to client engagement, building meaningful connections while navigating complex situations to enhance your personal brand and technical knowledge.
Responsibilities
- Utilize analytical techniques to address financial crime issues
- Engage with clients to foster meaningful professional relationships
- Apply SQL and Python for data analysis and problem\-solving
- Explore machine learning, NLP, and LLMs in relevant projects
- Contribute to team efforts while enhancing personal technical skills
- Adapt to complex situations and develop strategic insights
- Participate in research to support project objectives
- Uphold professional standards and ethical guidelines
What You Must Have
- Bachelor's Degree in Computer and Information Science, Computer and Information Science \& Accounting, Economics, Economics and Finance, Economics and Finance \& Technology, Engineering, Operations Management/Research, Statistics, Mathematics, Data Processing/Analytics/Science or related field
- 1 year of experience in data science/machine learning
What Sets You Apart
- Interest in financial crime, AML, and fraud analytics
- Skilled in SQL for complex data queries
- Advanced Python skills for data manipulation
- Experience building and deploying machine learning models
- Understanding of machine learning concepts and algorithms
- Comfort working with structured and unstructured data
- Familiarity with agentic AI frameworks
- Hands\-on experience with CI/CD pipelines for data science
- Proficiency in SQL and Python
- Basic understanding of machine learning algorithms and evaluation metrics
- Exposure to frameworks such as scikit\-learn, XGBoost, Hugging Face Transformers
- Other quantitative fields of study may be considered
The salary range for this position is: $63,000 \- $140,000\. Actual compensation within the range will be dependent upon the individual's skills, experience, qualifications and location, and applicable employment laws. All hired individuals are eligible for an annual discretionary bonus. PwC offers a wide range of benefits, including medical, dental, vision, 401k, holiday pay, vacation, personal and family sick leave, and more. To view our benefits at a glance, please visit the following link: https://pwc.to/benefits\-at\-a\-glance
As PwC is an equal opportunity employer, all qualified applicants will receive consideration for employment at PwC without regard to race; color; religion; national origin; sex (including pregnancy, sexual orientation, and gender identity); age; disability; genetic information (including family medical history); veteran, marital, or citizenship status; or, any other status protected by law.
PwC does not intend to hire experienced or entry level job seekers who will need, now or in the future, PwC sponsorship through the H\-1B lottery, except as set forth within the following policy: https://pwc.to/H\-1B\-Lottery\-Policy.
Learn more about how we work: https://pwc.to/how\-we\-work
For only those qualified applicants that are impacted by the Los Angeles County Fair Chance Ordinance for Employers, the Los Angeles' Fair Chance Initiative for Hiring Ordinance, the San Francisco Fair Chance Ordinance, San Diego County Fair Chance Ordinance, and the California Fair Chance Act, where applicable, arrest or conviction records will be considered for Employment in accordance with these laws. At PwC, we recognize that conviction records may have a direct, adverse, and negative relationship to responsibilities such as accessing sensitive company or customer information, handling proprietary assets, or collaborating closely with team members. We evaluate these factors thoughtfully to establish a secure and trusted workplace for all.
Salary Context
This $63K-$140K range is in the lower quartile for Data Scientist roles in our dataset (median: $162K across 211 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 3,824 AI roles we're tracking, Data Scientist positions make up 7% of the market. At PwC, 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 $200,000 based on 697 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $97,380. This role's midpoint ($101K) sits 49% below the category median. Disclosed range: $63K to $140K.
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.
PwC AI Hiring
PwC has 11 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Engineering Manager. Positions span New York, NY, US, Stamford, CT, US, Los Angeles, CA, US. Compensation range: $100K - $504K.
Location Context
AI roles in New York pay a median of $210,000 across 2,448 tracked positions. That's 5% above the national 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 3,824 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).
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 3,824 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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