Oracle's Financial Services Analytical Applications group is responsible for the development and marketing of financial industry specific marketing, profitability, risk management, fraud and regulatory compliance analytics applications. Our software is in over 70% of Global Systemically Important Financial Institutions (GSIFS). Our market leading financial crime and compliance software suite is used by over one hundred leading financial institutions globally and underpins Anti-Money Laundering, Operational Risk and Enterprise Fraud systems at some of the largest financial institutions in the world.
Our software is powered by advanced analytics (R, Apache Spark, Oracle Exadata), and our group employs data scientists, financial engineers, software developers and product managers, all of whom are focused exclusively on developing advanced analytical applications for the Financial Services Industry. We are the FINTEC group within Oracle.
All analytical applications we develop are developed to run on a common infrastructure platform, and the platform includes machine learning, Big Data processing and other core capabilities in order to support application use cases. We are looking for a senior data scientist with domain expertise in Financial crime and compliance analytics to lead the platform data sciences thrust. We wish to transform how the industry does business, leveraging the newest technologies, the latest advances in data science and the brightest minds. Join us and help realize our vision.
Brief Posting Description
We are building a world-class team of Data Scientists who will lead the development of the next generation advanced analytics platform that powers our Financial Crime and Compliance (FCCM) applications. You will be part of the core team of leaders who set the direction for the product suite. As a Senior Data Scientist, you will work in close collaboration with product mangers in the development and implementation of strategies for Fraud, AML and compliance applications, and you will lead the implementation of machine learning models. This is an opportunity to design, from the ground up, the next generation platform for our FCCM product suite.
Detailed Description This role requires broad knowledge of the current machine learning and statistical modeling methods and an understanding of how such methods are applied to Financial crime and compliance, specifically in Financial Services Industry. Prior experience as a data scientist in a bank, leading software vendor to the financial institutions or as a consultant to the industry is required. Knowledge of R and/or SAS/Python for data science is essential to success in this role as is prior experience in building machine-learning models using techniques such as ANN, Random Forest and Bayesian methods.
Application of machine learning in a large scale setting using distributed learning algorithms (Such as Apache Spark MLLIb) and over streaming data (Streaming regression, random forest,) is also desirable.
The person in this role will be responsible for building models that are used in our Fraud and AML applications. You will mentor other data engineers, data scientists and software engineers and you will work closely with our product managers in developing requirements and specifications for the software.
Job Requirements • Develop the next generation of predictive models for Financial crime and compliance subject area by combining machine learning and business domain expertise in Fraud and AML.
• Design rules and algorithms to identify fraudulent activity such as Identity Fraud and Cyber crime.
• Develop evaluation strategies to evaluate quality of detection algorithms.
• Develop self tuning models that work in a streaming data setting.
• Communicate requirements to the engineering and product teams to implement fraud strategies and models.
Although your focus will be on Financial Crime and Compliance, as a leader in the group, you will have the opportunity to work on and advice our Risk Management and Marketing analytics teams.
Qualifications: • PhD or Master's degree in Mathematics, Statistics, Computer Science, Operations Research, Physics or other quantitative discipline like Financial engineering.
• 2 years of AML or fraud analytics experience in the financial services industry
• 2 years of experience in building machine models in R, Python or SAS , using techniques such as Random Forest, ANN, SVM, logistic regression.
• Knowledge of implementing streaming models (dynamic / incremental models) such as streaming K-means and Streaming Random Forest is desired.
• Hands-on expertise with SQL databases such as Oracle is required.
• Knowledge of Apache Hadoop ecosystem, Complex Event Processing engines, Apache Spark, Spark MLlib and streaming systems is highly desired.
• Excellent written and oral communication skills
Designs, develops and programs methods, processes, and systems to consolidate and analyze unstructured, diverse "big data" sources to generate actionable insights and solutions for client services and product enhancement.
Interacts with product and service teams to identify questions and issues for data analysis and experiments. Develops and codes software programs, algorithms and automated processes to cleanse, integrate and evaluate large datasets from multiple disparate sources. Identifies meaningful insights from large data and metadata sources; interprets and communicates insights and findings from analysis and experiments to product, service, and business managers.
Acknowledged authority within the Corporation. Acts as a leader of large-scale company initiatives. Viewed by peers as a leader and top contributor and by line management as a key business partner. 10 plus years experience. BA/BS degree preferred.
Oracle is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans status or any other characteristic protected by law.
A little about us:
Oracle is shifting the complexity from IT, moving it out of the enterprise by engineering hardware and software to work together—in the cloud.