Global Financial Crimes Manager

Do you want your voice heard and your actions to count?

Discover your opportunity with Mitsubishi UFJ Financial Group (MUFG), one of the world’s leading financial groups. Across the globe, we’re 120,000 colleagues, striving to make a difference for every client, organization, and community we serve. We stand for our values, building long-term relationships, serving society, and fostering shared and sustainable growth for a better world.

With a vision to be the world’s most trusted financial group, it’s part of our culture to put people first, listen to new and diverse ideas and collaborate toward greater innovation, speed and agility. This means investing in talent, technologies, and tools that empower you to own your career.

Join MUFG, where being inspired is expected and making a meaningful impact is rewarded.

The selected colleague will work at an MUFG office or client sites four days per week and work remotely one day. A member of our recruitment team will provide more details.

Acting as an Innovation Solutions Engineer responsible for applying artificial intelligence knowledge (machine learning, natural language processing, graph analytics, robotics, optical character recognition) to optimize technology tools in different Financial Crimes areas (Transaction Monitoring, KYC, Payment Screening, Analytics). Applying data science principles and/or business process re-engineering principles to help assess  innovative credible vendor products in the key Financial Crimes areas of transaction monitoring, investigations, screening, KYC, and analytics; application of data science principles to help evaluate vendor products including creating and optimizing (false positive reduction) descriptive and predictive supervised and unsupervised machine learning models using Python or R for transaction monitoring and customer due diligence (KYC). Identifying and evaluating emerging and other innovative technology solutions on a continuous basis in order to improve functional, reporting, intelligence, and analytics capabilities in different Financial Crimes areas. Applying knowledge of data analysis by querying big data (Swift payment messages, and KYC data elements) using Python or R (minimum 10 Terabytes); performing data transformation and cleanup for data usage in machine learning models, and migrating data from on premise to cloud environments (AWS RDS). Setting up and serving as the initial point of contact with MUFG Technology groups to establish  cloud trial environments (including S3 and EC2 buckets, Sagemaker, and AWSGlue) (sandbox or production environments), including estimating internal and external costs associated with environment setup (hardware, software, storage, and license) and working with architecture teams to identify optimum technical stacks for both innovation trials and production implementation. Serving as the initial point of contact for vendors soliciting business with the Financial Crimes Division; translating advanced technology experience, the enterprise technological environment, and strategic technological opportunities and requirements into technical engineering solutions. Serving as the initial innovation contact for Global Financial Crimes Compliance colleagues, including providing advice on innovation solutions, creating use cases and business requirement documents, and correlating internal needs with external solution availability. Working collaboratively across functional teams within GFCD and with vendors to ensure successful software trials and proofs of concept, including identification of data necessary for trials, articulation of trial use cases, and coordination of trial execution. Performing cost-benefit analysis of potential vendor solutions, including license cost, internal implementation cost, and user cost savings. Reviewing technical components of the vendor’s solution and identifying technical risks. Assisting in the creation of software assessment presentations as well as business requirements documents and other reporting documents to bank management in order to support the evaluation process. Ensuring a globally consistent and continuously innovative approach that engages the Bank’s stake holders, financial crimes technology companies and other related industries is followed. Monitoring areas of innovation in the banking industry specifically targeting data science relevant processes for proactive financial crime risk identification, and ascertain suitability for implementation within MUFG.

Qualifications – External

Education: Bachelor’s degree in Computer Science, Computer Engineering, Information Systems, or Analytics (or foreign equivalent degree).

Experience: 4 years of financial crimes data scientist experience in the Banking or Financial Services industry creating and optimizing (false positive reduction) descriptive and predictive supervised and unsupervised machine learning models using Python or R for transaction monitoring and customer due diligence (KYC) in the anti-money laundering (AML space); querying and analyzing  big data (Swift payment messages, and KYC data elements) using Python or R (minimum 10 Terabytes); and transforming and migrating data from on premise to cloud environments; and 2 years of experience must include assessing vendors in the artificial intelligence space (including traditional machine learning, generative AI and graph analytics) to determine if vendor offerings meet use cases; and 1 year of experience must include setting-up AWS cloud environment including S3 and EC2 buckets, Sagemaker, and AWSGlue.

Other: Required to participate in calls with counterparts in Asia 1-2 nights a week.     

Other: Position requires employment in-office 4 days per week and remotely 1 day per week.

Wage: $124,426 - $180,000 U.S. dollars per year

Employer: MUFG Bank, Ltd.

Location: Irving, TX 75038

Reference internal requisition #10067269-WD.

We are committed to leveraging the diverse backgrounds, perspectives and experience of our workforce to create opportunities for our people and our business; Equal Opportunity Employer: Minority/Female/Disability/Veteran.

We will consider for employment all qualified applicants, including those with criminal histories, in a manner consistent with the requirements of applicable state and local laws (including (i) the San Francisco Fair Chance Ordinance, (ii) the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, (iii) the Los Angeles County Fair Chance Ordinance, and (iv) the California Fair Chance Act) to the extent that (a) an applicant is not subject to a statutory disqualification pursuant to Section 3(a)(39) of the Securities and Exchange Act of 1934 or Section 8a(2) or 8a(3) of the Commodity Exchange Act, and (b) they do not conflict with the background screening requirements of the Financial Industry Regulatory Authority (FINRA) and the National Futures Association (NFA). The major responsibilities listed above are the material job duties of this role for which the Company reasonably believes that criminal history may have a direct, adverse and negative relationship potentially resulting in the withdrawal of conditional offer of employment, if any.

The above statements are intended to describe the general nature and level of work being performed. They are not intended to be construed as an exhaustive list of all responsibilities duties and skills required of personnel so classified.

We are proud to be an Equal Opportunity Employer and committed to leveraging the diverse backgrounds, perspectives and experience of our workforce to create opportunities for our colleagues and our business. We do not discriminate on the basis of race, color, national origin, religion, gender expression, gender identity, sex, age, ancestry, marital status, protected veteran and military status, disability, medical condition, sexual orientation, genetic information, or any other status of an individual or that individual’s associates or relatives that is protected under applicable federal, state, or local law.