Salary $60000 - $140000 per year + Bonus
Location New York
Consultant Wesley Darbouze
Date posted June 20, 2017
My client is a top tier multinational bank and financial services company with operations in retail, wholesale and investment banking, as well as wealth management, mortgage lending and credit cards.
*Please note that there are two vacancies for this position. A junior vacancy in addition to an AVP level Data Scientist vacancy*
Data Scientist, Financial Crime Analytics
*Please note that there is both a junior vacancy in addition to an AVP level Data Scientist vacancy*
Financial Crime Overview
The Financial Crime team helps ensure that My client complies with Anti-Bribery & Corruption (ABC), Anti-Money Laundering (AML) and Sanctions legislation.
Data Scientists within this team are responsible for understanding the business area, data and existing systems and providing solutions ranging from Transaction Monitoring system optimization, the development of alert prioritization/scoring models and algorithms, generation of complex insights and actionable MI – all in the pursuit of enhancing the effectiveness and efficiency of AML program.
This position provides a great growth opportunity for a junior Data Scientist. Although some hands-on analytics experience in a corporate environment will be seen as advantageous, as a junior Data Scientist you will be provided with the opportunity to develop the wide array of skills and experiences needed through working alongside more experienced Data Scientists and relevant training opportunities.
Key Accountabilities and Skills required:
- Provide analytics support to the AML Transaction Monitoring team in areas such as threshold tuning/optimization, customer/account segmentation and data-driven decision making and insights. This will involve techniques such as hypothesis testing, regression analysis, optimization methods and clustering analysis.
- Engage in a range of innovative PoC/Prototype R&D activities including the data-driven automation of various currently-manual processes, the development of alert/case scoring & prioritization models and the generation of enhanced AML detection capabilities through the application of traditional statistical models (regressions etc) and more complex machine learning techniques.
- Support the growth and scope of the Financial Crime Analytics team through the generation of ideas. This will involve engaging with key stakeholders to identify their key problems and needs, and keeping up-to-date with external industry development through own research and attending key peer-group meetings and conferences.
- Provide analytics support to the AML Investigations team in areas such as the development of case-prioritization scoring processes, enhanced alert-case merging and ad-hoc insight requests.
- Support in activity relating to the Banks Model Risk Management policy where required.
- Engage with internal Technology team to provide requirements on the development of strategic data infrastructure ensuring that our infrastructure capability aligns to the needs of the Financial Crime Analytics team as they’ll as to the needs of our wider stakeholders.
You will be provided with the opportunity to develop the wide array of skills and experiences needed through working alongside experienced Data Scientists and training opportunities.
Basic Qualifications, Skills & Experience
- A Masters degree in Data Science or similar preferred, however, a Bachelors in a quantitative discipline with a significant statistical/data analytics component (Statistics, Mathematics, Operational Research, Computer Science, Computational/Mathematical Finance) will also be considered.
Preferred Qualifications, Skills & Experience
- Understanding of/exposure to a range of statistical and machine learning techniques (e.g. hypothesis testing, regression, clustering, decision trees, machine learning models).
- Exposure to common Python libraries for data manipulation, statistical analysis and machine learning (Pandas, Scikit, TensorFlow, h2o.io etc) desirable.
- Experience with visualization tools (e.g., Spotfire, Tableau) beneficial.
- Exposure/experience with distributed-data architecture (Hadoop/MapReduce, Spark) and cloud architecture such as AWS a plus.