If you are building a career where data science, machine
learning, and financial crime prevention intersect, the AML/Sanctions Data
Scientist Associate opportunity at PwC is worth exploring. This role is
designed for an early-career data professional who wants to apply analytical
and programming skills to real-world financial crime challenges while working
in a client-focused consulting environment.
The AML/Sanctions Data Scientist Associate at PwC combines data analysis with areas such as anti-money laundering (AML), sanctions, fraud analytics, machine learning, natural language processing, and emerging AI technologies. Rather than focusing only on traditional data science exercises, the position offers exposure to complex datasets and business problems where analytical findings can support better risk-related decisions.
The role is listed as full-time and is available across
several U.S. locations, including Seattle, Washington, as well as Charlotte,
Washington D.C., Boston, New York, and Philadelphia. PwC lists a requirement of
at least one year of experience in data science or machine learning, making
this an opportunity particularly relevant to professionals who have already
gained some practical experience and want to expand into financial crime
analytics.
For qualified candidates, the position also provides
exposure to SQL, Python, machine learning frameworks, unstructured data, AI
technologies, and client engagements.
Job Overview
|
Job Detail |
Information |
|
Company |
PwC |
|
Position |
AML/Sanctions Data Scientist – Associate |
|
Primary Location |
Seattle, Washington |
|
Other Locations |
Charlotte, Washington D.C., Boston, New York, Philadelphia |
|
Employment Type |
Full-time |
|
Job Category |
Data and Analytics |
|
Specialty |
Data, Analytics & AI |
|
Level |
Associate |
|
Experience Required |
At least 1 year in data science or machine learning |
|
Education |
Bachelor's degree in a relevant quantitative, technology,
engineering, economics, statistics, mathematics, or related field |
|
Travel |
Up to 60% |
|
Salary Range |
$63,000–$140,000, depending on qualifications, skills,
experience, location, and applicable laws |
The salary range and employment details above are based on
the available job posting. Actual compensation can vary between candidates and
locations.
About PwC
PwC is a global professional services organization that
works with businesses and other organizations across areas such as consulting,
audit and assurance, tax, technology, risk, and transformation.
Its consulting work increasingly involves technology and
data-driven decision-making. That makes data science an important capability
for solving complex business problems. Professionals working in this
environment may encounter different industries, datasets, technologies, and
client requirements rather than working on one narrowly defined product.
For someone entering financial crime analytics, this type of
environment can provide an opportunity to understand how technical analysis
connects with business processes, risk management, compliance, and client
needs.
The PwC opportunity is particularly interesting because the
role sits at the intersection of Data & Analytics, AI, and financial
crime. Candidates can therefore develop both technical and business-facing
capabilities during their careers.
Key Responsibilities
Apply Data Science to Financial Crime Problems
The associate will use analytical approaches to investigate
and address financial crime-related challenges. This can involve working with
large datasets, identifying patterns, evaluating information, and helping teams
turn complex data into useful insights.
Work With SQL and Python
SQL and Python are central technical skills for this
position. SQL can be used to retrieve, transform, and analyze data, while
Python can support data manipulation, modeling, automation, and analytical
workflows.
Candidates should therefore be comfortable moving between
database queries and Python-based analysis.
Explore Machine Learning and AI
The position provides exposure to machine learning, natural
language processing, and large language model technologies. This can be
particularly valuable for professionals interested in how modern AI techniques
can be applied to risk and compliance problems.
The posting also mentions exposure to agentic AI frameworks,
making familiarity with emerging AI approaches a potential advantage.
Analyze Structured and Unstructured Data
Financial crime investigations may involve different types
of information. Structured datasets can be analyzed using conventional database
and statistical methods, while unstructured information may require additional
processing techniques.
Understanding how to work with both types of data can help
candidates become more versatile data scientists.
Support Client Engagements
Because PwC operates in a consulting environment, the
position involves working with clients and project teams. Strong communication
is therefore important alongside technical ability.
Associates may need to explain analytical findings, ask
questions, understand business requirements, and collaborate with colleagues
who have different areas of expertise.
Participate in Research and Problem Solving
The role also involves research that supports project
objectives. This may require learning unfamiliar technologies, understanding a
new business problem, evaluating possible analytical approaches, and adapting
to changing project requirements.
Maintain Professional Standards
The position requires employees to follow professional and
ethical expectations while working with client information and sensitive
business problems. Attention to detail, responsible data handling, and sound
professional judgment are important qualities.
Required Skills
Technical Skills
Candidates should focus on developing the following areas:
- SQL
and complex data querying
- Python
programming
- Data
manipulation and analysis
- Machine
learning fundamentals
- Machine
learning model development
- Model
evaluation and performance metrics
- Structured
and unstructured data processing
- Data
science workflows
- Statistical
and quantitative analysis
The posting specifically identifies technologies and
frameworks such as scikit-learn, XGBoost, and Hugging Face Transformers
as useful areas of exposure.
Soft Skills
Technical knowledge alone is not enough for a
client-oriented data science role. Important professional skills include:
- Clear
written and verbal communication
- Analytical
thinking
- Curiosity
and willingness to learn
- Collaboration
- Adaptability
- Active
listening
- Problem
solving
- Ability
to ask effective questions
- Attention
to quality and detail
- Professional
judgment
Preferred Skills
A background or interest in AML, sanctions, fraud
analytics, financial crime, machine learning, NLP, and AI can make a
candidate's profile more relevant.
Experience with CI/CD pipelines for data science and agentic
AI frameworks can also strengthen an application.
Qualifications
The position requires a bachelor's degree in a
relevant discipline. The listed fields include computer and information
science, economics and finance, engineering, operations management or research,
statistics, mathematics, data processing, data analytics, data science, and
related areas.
PwC also specifies at least one year of experience in
data science or machine learning.
Importantly, candidates should review the official posting
carefully because eligibility, employment requirements, and sponsorship
policies can affect individual applications.
Salary, Benefits and Perks
According to the available job posting, the salary range for
this position is $63,000 to $140,000. The actual compensation depends on
factors such as skills, experience, qualifications, location, and applicable
employment laws.
The posting also states that hired employees are eligible
for an annual discretionary bonus.
Listed benefits include areas such as:
- Medical
coverage
- Dental
coverage
- Vision
coverage
- 401(k)
- Holiday
pay
- Vacation
- Personal
and family sick leave
- Additional
employee benefits
Benefits can vary depending on location and employment
terms, so applicants should verify the current details directly through PwC's
official careers and benefits information.
Why Consider This Opportunity?
Build a Financial Crime Analytics Career
Financial crime analytics combines business, compliance,
statistics, and technology. For a data scientist interested in this
intersection, the role can provide a practical way to develop domain knowledge
while continuing to strengthen technical skills.
Expand Your AI and Machine Learning Knowledge
The position goes beyond basic reporting and analytics.
Exposure to machine learning, NLP, LLMs, and AI frameworks can help
professionals understand how newer technologies are being evaluated for complex
analytical use cases.
Gain Client-Facing Experience
Consulting work can develop skills that are difficult to
gain from purely internal data roles. Communicating with clients, understanding
requirements, presenting findings, and adapting analysis to different problems
can make a data professional more versatile.
Work With Multiple Types of Problems
The role can involve different clients and challenges. This
variety may suit professionals who prefer learning through new projects rather
than performing the same analytical workflow repeatedly.
Develop Both Technical and Business Skills
One of the strongest aspects of the position is the
combination of technical data science and commercial awareness. Candidates can
learn not only how to build analytical solutions but also why those solutions
matter to an organization.
Hiring Process
The exact selection process can change depending on the
position, business needs, and candidate. Applicants should not assume that
every candidate will experience the same sequence.
A typical professional hiring process may include:
- Online
application – Submit your resume and required information through the
official careers site.
- Resume
screening – Recruiters or hiring teams review qualifications against
the position requirements.
- Initial
conversation – Some candidates may be contacted for an introductory
discussion.
- Technical
or behavioral interviews – Depending on the role, interviews may
explore technical knowledge, analytical thinking, communication, and
relevant experience.
- Additional
assessment or interviews – Some hiring processes may include further
evaluations.
- Final
decision and offer – Selected candidates receive information about
compensation, employment conditions, and next steps.
The above is a general hiring-process guide and is not a
claim about PwC's exact process for every applicant.
How to Apply
Interested candidates should apply through the official
PwC careers listing rather than relying on third-party job boards or
unofficial recruiters.
Official application: Apply
for the AML/Sanctions Data Scientist Associate position at PwC
Before submitting an application, review your resume for
relevant keywords such as SQL, Python, machine learning, data science, NLP, AI,
fraud analytics, and financial crime where they genuinely match your
experience.
Candidates should also verify the current job status,
location availability, eligibility requirements, compensation, and sponsorship
information on the official posting.
Frequently Asked Questions
1. Who can apply for the AML/Sanctions Data Scientist
Associate role?
Candidates with a relevant bachelor's degree and at least
one year of data science or machine learning experience may be eligible,
subject to the other requirements listed by PwC.
2. Where is the position located?
The position is listed in Seattle, Washington, with
additional locations in Charlotte, Washington D.C., Boston, New York, and
Philadelphia.
3. Is this a remote job?
The provided posting does not establish that the position is
fully remote. Applicants should check the current official listing for the
applicable work arrangement.
4. What degree is required?
A bachelor's degree in a relevant field such as computer
science, data science, statistics, mathematics, engineering, economics,
operations research, or a related discipline is listed.
5. Is prior experience mandatory?
The posting states that candidates must have at least one
year of experience in data science or machine learning.
6. Which programming skills are important?
SQL and Python are particularly important. Candidates can
also benefit from knowledge of machine learning frameworks and analytical
tools.
7. Is financial crime experience required?
The posting identifies interest in financial crime, AML, and
fraud analytics as skills that can distinguish candidates. Applicants should
review the official requirements to determine how their background fits the
role.
8. What machine learning technologies are useful?
The posting mentions machine learning concepts and
frameworks including scikit-learn, XGBoost, and Hugging Face Transformers.
Familiarity with NLP, LLMs, and agentic AI can also be relevant.
9. How much does the position pay?
The listed salary range is $63,000–$140,000. Actual
compensation depends on qualifications, experience, location, and other
applicable factors.
10. Does the job require travel?
Yes. The available posting lists travel requirements of up
to 60%.
11. Is relocation assistance available?
The supplied job information does not confirm relocation
assistance. Candidates should ask PwC recruiting or review the applicable
employment information before making relocation decisions.
12. Where should candidates apply?
Applicants should use the official PwC careers page linked
in this article. Avoid submitting sensitive personal information through
unofficial job websites.
Related Job Suggestions
If this position matches your career interests, you may also
want to search for:
- AML
Data Scientist
- Financial
Crime Data Analyst
- Fraud
Analytics Data Scientist
- Machine
Learning Engineer – Risk Analytics
- Data
Scientist – Financial Services
- Sanctions
Analytics Specialist
- Risk
Analytics Consultant
- AI/ML
Data Scientist
Final Thoughts
The AML/Sanctions Data Scientist Associate at PwC is
a strong-fit opportunity for early-career data professionals who want to
combine technical analytics with financial crime and risk-related challenges.
The role calls for practical SQL and Python skills while offering exposure to
machine learning, NLP, LLMs, and other emerging AI technologies.
It also goes beyond technical work by emphasizing
communication, client interaction, research, adaptability, and professional
development. That combination can be valuable for candidates who eventually
want to move toward senior data science, financial crime analytics, AI
consulting, or risk technology positions.
Because job requirements, compensation, locations, and eligibility policies can change, applicants should always verify the latest information on the official PwC careers page before applying.
