Senior Data Analyst- Fraud & AML

Reposted 5 Days Ago
2 Locations
In-Office
148K-220K Annually
Senior level
Information Technology
The Role
The Senior Data Analyst will develop AML and fraud models using Python and SQL, maintain performance dashboards, and lead data initiatives to improve regulatory compliance in financial services.
Summary Generated by Built In
ABOUT xAI

xAI’s mission is to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge. Our team is small, highly motivated, and focused on engineering excellence. This organization is for individuals who appreciate challenging themselves and thrive on curiosity. We operate with a flat organizational structure. All employees are expected to be hands-on and to contribute directly to the company’s mission. Leadership is given to those who show initiative and consistently deliver excellence. Work ethic and strong prioritization skills are important. All employees are expected to have strong communication skills. They should be able to concisely and accurately share knowledge with their teammates.

ABOUT THE ROLE:

We are looking for a Senior Data Scientist to join our Compliance Program and play a pivotal role in modernizing and strengthening our financial crime detection capabilities. You will architect, build, and optimize data-driven transaction monitoring models, coverage assessment frameworks, and advanced analytics solutions that directly support BSA/AML regulatory compliance, including but not limited to SAR filing, Customer Identification Program elements, and Enhanced Due Diligence measures. The role will also support the OFAC Sanctions, Fraud and overall risk prioritization across multiple products and jurisdictions.

This is a high-impact, cross-functional role that blends advanced analytics, machine learning, and deep compliance domain expertise. You will work closely with Compliance, Engineering, Model Risk, Product, and external regulators to ensure our controls are robust, defensible, and scalable.

RESPONSIBILITIES:
  • Design, develop, and enhance AML and fraud models, rules, and heuristics using Python, SQL, and AI-enabled tooling; partner with the Compliance Machine Learning team on model reviews to improve detection rates and reduce false positives.
  • Build and maintain interactive performance dashboards  and automated reporting solutions that track key risk, productivity, and capacity metrics for senior leadership and regulators.
  • Architect and implement enterprise-wide Transaction Monitoring Coverage Assessment frameworks, including standardized methodologies for gap identification, root-cause analysis, remediation planning, and ongoing sustainability monitoring.
  • Lead complex data initiatives, including extraction of SAR filing metrics with product-level breakdowns and development of jurisdiction- and typology-specific SAR narrative generator tools.
  • Embed data science best practices into product launches and feature rollouts to proactively identify and close monitoring coverage gaps.
  • Support regulatory examinations (e.g., NYDFS Part 504) by preparing analytical documentation, third-party validation materials, and executive certification packages.
  • Drive continuous improvement of compliance operations through automation, process optimization, and advanced analytics.
BASIC QUALIFICATIONS:
  • 7+ years of hands-on data science / advanced analytics experience in financial services, with at least 4 years focused on fraud and financial crime compliance.
  • Master’s degree (or higher) in Applied Mathematics, Statistics, Data Science, Actuarial Science, or a related quantitative field.
  • Proven track record of building and optimizing transaction monitoring models, coverage frameworks, or compliance analytics programs in a regulated environment (fintech, bank, or payment company preferred).
  • Deep understanding of BSA/AML regulations, suspicious activity reporting, customer due diligence, sanctions screening, and model risk management principles.
  • Demonstrated ability to translate complex regulatory requirements into actionable data solutions and present findings to senior leadership and regulators.
  • Certified Anti-Money Laundering Specialist (CAMS) or equivalent compliance certification is strongly preferred.
PREFERRED SKILLS AND EXPERIENCE:
  • Experience leading cross-functional initiatives involving Engineering, Legal, Product Compliance, and external consulting partners.
  • Background in building internal case management systems, SAR automation tools, or RPA solutions.
  • Familiarity with AML detection platforms.
  • Track record of delivering measurable impact (e.g., reduced case volumes, improved detection of high-risk activity, increased operational efficiency).
COMPENSATION AND BENEFITS:

$148,000- $220,000 USD

Base salary is just one part of our total rewards package at xAI, which also includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short & long-term disability insurance, life insurance, and various other discounts and perks.

ITAR REQUIREMENTS:
  • To conform to U.S. Government export regulations, applicant must be a (i) U.S. citizen or national, (ii) U.S. lawful, permanent resident (aka green card holder), (iii) Refugee under 8 U.S.C. § 1157, or (iv) Asylee under 8 U.S.C. § 1158, or be eligible to obtain the required authorizations from the U.S. Department of State. Learn more about the ITAR here. 

xAI is an equal opportunity employer. For details on data processing, view our Recruitment Privacy Notice.

Skills Required

  • 7+ years of hands-on data science experience in financial services
  • 4 years focused on fraud and financial crime compliance
  • Master's degree in Applied Mathematics, Statistics, Data Science, or related field
  • Proven track record of building and optimizing transaction monitoring models
  • Deep understanding of BSA/AML regulations
  • Certified Anti-Money Laundering Specialist (CAMS) or equivalent
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The Company
HQ: San Francisco, CA
96 Employees

What We Do

Understand the Universe

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