Freelance Senior AML (Anti-Money Laundering) Data Scientist

Do you want to be a part of a one-of-a-kind initiative to tackle money laundering and terrorist financing at a wide scale?

TMNL (Transaction Monitoring Netherlands) was established by the five largest Dutch banks, and is the 1^st initiative in the world fighting financial crime in a multi-bank setting. This enables us the unique opportunity to develop advanced models, covering the full network of transactions of most businesses in The Netherlands. As a senior data scientist, you will play a pivotal role in developing state-of-the-art models for solving the most difficult anti-money laundering (AML) problems.

You will have the opportunity to collaborate with stakeholders from the banks, the public sector, and the law enforcement agencies, to collectively defeat financial crime. Leveraging your expertise in data science and machine learning, you will uncover new insights and create effective detection methods that help identify unusual (financial) behaviour. As a member of the data science community, you will spar and innovate with a diverse group of international professionals who are passionate about making a difference. Our multidisciplinary model development teams are composed of scientists, engineers, and AML experts from various backgrounds. You'll have the chance to learn from each other; share knowledge; and tackle complex challenges together.

If you're ready to join a dynamic team; work on socially impactful challenges; and be a part of a mission-driven organisation, then we invite you to apply for this position at TMNL. Help us revolutionize transaction monitoring by uncovering hidden money laundering patterns. Let’s make this world a safer place by protecting its financial system!

Your day at TMNL:

• Understand a FEC (Financial Economic Crime) topic or money laundering typology by having a knowledge sharing session with an AML expert colleague
• TBML (Trade Based Money Laundering) for instance
• Brainstorm with your data science colleagues to come up with modelling ideas for capturing a particular FEC risk
• Perform an exploratory analysis on transaction (related) business data of the 5 largest Dutch banks
• Design and execute experiments for your AML model
• Fine-tune a network (graph theory) model to capture anomalous interactions among business entities
• Participate in an architecture board meeting to standardize concepts related to a model or technique, with the goal of turning it into a product (or a service)
• Extend the design of our in-house (temporal) graph framework for detecting a particular money laundering typology
• FaSTM∀N is a state-of-the-art framework for discovering unknown money laundering schemes
• Ask us why it is called FaSTM∀N during one of the interviews 😉
• Work alongside a machine learning engineer colleague to develop the production pipeline for your model
• As an expert in your field, mentor and guide your junior data science colleagues with any day-to-day challenges they face
• Lead a meeting with the bank stakeholders for gathering the feedback from the last batch of delivered alerts
• Explore new areas where data science can improve or accelerate the fight against money laundering

Must-have:

• Around 10 years of practical experience in the field of data science
• A master's degree in artificial intelligence, statistics, data science, mathematics, computer science, physics, engineering or a related quantitative field
• Excellent Python, Spark, and SQL skills
• Excellent communication skills:
• Ability to present complex topics to non-technical audiences
• Ability to gather and translate requirements from business users into a plan of approach
• Proven record of developing machine learning models and production rising them at scale:
• Experience in building customer facing DS products
• Experience building DS solutions as part of a software development team
• Ability to scope projects and prioritise tasks
• Experience in the financial sector, with AML, KYC, FEC or transactionmonitoring 
• Currently living in the Netherlands

Nice-to-have:

• Experience with graph analytics and anomaly detection
• In addition to familiarity with tools like igraph, Graphframes, NetworkX
• PhD in related field
• Knowledge of agile software development tools and methodologies
• Git, DevOps, continuous integration/deployment tools, etc.
• Knowledge of software design principles
• Experience with working on AWS or other major cloud platforms
• Experience using privacy enhancing technologies

About TMNL

At Transaction Monitoring Nederland we are creating a top-notch detection and transaction monitoring company to defeat financial crimes such as money laundering and the financing of terrorism. We are forming an energetic and mission-driven team, where the curious and talented minds in anti-money laundering (AML) and financial crime detection, data science, data engineering, advanced analytics, and artificial intelligence join forces.

Our 4 key values are an important part of our culture. We are: Courageous in Innovation, Curious in Collaboration, Persistent in Responsibility, and we take Care of each other. We aim to achieve a long-lasting societal change by exposing the craftiest bad actors in the financial world.

On offer

At TMNL you will experience the freshness, the freedom, the mandate, and the pace forward of a scale-up. We’re based on a healthy foundation, as we have the full dedication and support of the five largest Dutch banks from the start. We love our work, and we also enjoy a healthy work-life balance. The assignment will be for 6 months with an option of an extension. Your rate will in accordance with your experience.

Interested?

Apply and meet our Data Science team,  Dennis Timmers, Haseeb Tariq and Lucian Baghiuc (Chief AML Analytics Officer) at TMNL. Click the apply button or contact Silvia Szabo, HR Admin Specialist, at silvia.szabo@tmnl.nl for more information. We only accept applications from independent freelance candidates, as we only work with agencies accepted by us.

We encourage all qualified applicants, including minorities; people with disabilities; and members of underrepresented groups.

Recruitment Agency?

Acquisition is not necessary, as we do our own recruitment. Thanks!