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PwC

AML/Sanctions- Data Scientist- Associate

Sorry, this job was removed at 07:17 p.m. (PST) on Thursday, Aug 13, 2026
Hybrid
New York, NY
63K-140K Annually
Junior
Hybrid
New York, NY
63K-140K Annually
Junior

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Join PwC's Financial Crime Unit to analyze complex datasets using SQL and Python to detect AML/sanctions issues. Contribute to client engagements, explore ML, NLP and LLM approaches, build and deploy models, participate in research, and uphold professional and ethical standards while developing technical and commercial skills.
The summary above was generated by AI
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.

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