Senior Data Scientist CRO APAC
- Employer
- Credit Suisse
- Location
- Singapore, Singapore
- Salary
- Competitive
- Closing date
- Dec 19, 2019
View more
- Job Function
- Compliance/Regulatory
- Industry Sector
- Finance - General
- Employment Type
- Full Time
- Education
- Bachelors
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We Offer
The Analytics & Data Science team is part of Credit Suisse's Global Compliance Division. Our work provides decision-makers across the globe with useful metrics, insights, predictions and analytic tools, enabling a data- and technology-driven compliance function. Partnering closely with subject matter experts and business partners, we use machine learning techniques to work on solutions for transaction monitoring, client intelligence, employee surveillance and automated reporting.
You Offer
The Analytics & Data Science team is part of Credit Suisse's Global Compliance Division. Our work provides decision-makers across the globe with useful metrics, insights, predictions and analytic tools, enabling a data- and technology-driven compliance function. Partnering closely with subject matter experts and business partners, we use machine learning techniques to work on solutions for transaction monitoring, client intelligence, employee surveillance and automated reporting.
- You will have the opportunity to become part of our highly motivated Analytics team for Credit Suisse based in Singapore focusing on building and driving new and state of the art technologies in various fields
- An exciting role for you as Senior Data Scientist within our Analytics & Data Science Lab and close collaboration with its Chief Operating Officer, Financial Crimes Compliance, IT and Operations teams as well as coordination with related organizations within our Bank
- Responsibility for developing, maintaining and extracting knowledge from strategic internal and external data sets
- Studying fundamental and high impact business questions that directly affect the direction of the company and the industry at large
- Developing and designing algorithms, building prototype versions, running multiple validations with business experts and working on their operationalization
- Becoming part of an open-minded team with a strong team spirit in a versatile and flexible working environment
- Flexible/agile working options are possible
You Offer
- You hold a PhD or Master's degree in a quantitative field (e.g. Statistics, Mathematics, Physics, Economics), Computer Science or an equivalent education
- You have at least 3-5 years of experience in applying statistical modelling, machine learning and / or exploratory analysis to large datasets: classification, scorecard models, segmentation, clustering, Bayesian statistics, anomaly detection, NLP
- You have an experience in the regulatory risk technologies domain within the financial industry is considered a plus
- You are proficient in at least one of the following: Python, R, SQL
- Experience using big data platforms including the development of big data pipelines, relational database programming and distributed data processing at scale e.g. Spark or Hive
- You are familiar with assembling and analyzing data sets from disparate sources applying quantitative methodologies, computational frameworks and systems
- You are experienced in Financial Crime Compliance or Transaction Monitoring is a plus.
- You have a basic understanding of Transaction Monitoring, AML risk types, Typologies, red flags to be able to speak in the language of the user community
- You have strong communication and stakeholder management capabilities, the ability to effectively collaborate and build professional relationships across all organizational levels
- Integrity, responsibility and confidentiality required for dealing with sensitive data
- Demonstrable experience as a strong, ambitious, standout colleague and independent thinker, willing to co-operate in a highly collaborative environment and contribute to the team's success
- Experience leading and managing others
- Willingness to assume additional responsibilities and to become a respected knowledge carrier within the team
- Relevant professional designations (e.g., CFA, FRM, FSA, CIMA or PMP) are considered an advantage
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