In Scope of Position based Promotions (INTERNAL only)

Job Title: Data Scientist AFC - Analytics Analyst

Corporate Title: Analyst

Location: Bangalore, India

Role Description

Anti-Financial Crime (AFC) is responsible for the prevention of money laundering, the fight against terrorist financing, the monitoring of financial and commercial sanctions, and the fight against other criminal activities.

AFC's goal is to identify risks resulting from financial crimes that could jeopardize Deutsche Bank's integrity and thus its success, to prevent these risks as effectively as possible, or, if they do occur, to investigate and clear them up appropriately in the interest of all parties involved.

Within AFC's entire value chain, the AFC Model Development Germany/EMEA area is at the heart of the game as it uses a risk-based management approach to create the models and scenarios that translate the above goals and expectations into concrete controls and analysis.

What we’ll offer you

As part of our flexible scheme, here are just some of the benefits that you’ll enjoy,

• Best in class leave policy.
• Gender neutral parental leaves
• 100% reimbursement under childcare assistance benefit (gender neutral)
• Sponsorship for Industry relevant certifications and education
• Employee Assistance Program for you and your family members
• Comprehensive Hospitalization Insurance for you and your dependents
• Accident and Term life Insurance
• Complementary Health screening for 35 yrs. and above

Your key responsibilities

As Analytics Analyst / Data Scientist within AFC Model Development Germany/EMEA, you are responsible for incorporating complex business requirements into implementable models for known pattern or anomalous behaviour and for compliance with Deutsche Bank's modelling standards. For this role you should have solid experience as data scientist and modeller and sound programming skills.

• Development of money laundering detection models in Python, R, Apache Spark or Scala
• Build an understanding of the bank's existing data pipelines and data sources, with the aim to leverage the full potential of existing and new data
• Development and administration of the pipeline for automatic execution of the standardized model development steps
• Cooperation with business experts, investigators, architects, infrastructure experts and developers on an international strategic intelligence framework for money laundering prevention - from data analysis to scalable models
• Documentation of methodology, models and source code
• Support and quality assurance during the test and development phase

Your skills and experience

• You have a degree in mathematics, physics, computer science, or comparable qualifications and experience
• A real interest in Anti-Financial-Crime topics such as AML, name screening, or CTF and basic knowledge on those topics
• Experience with modelling with transaction and related data form structured and unstructured sources based on a solid understanding of the statistical and geometrical background of the models
• You have at least 2 years of experience in Python, PySpark, SparkR or Spark or similar programming languages with a focus on Big Data (Hadoop, Spark, Kafka, Solr etc.); Experience with shell scripting, test frameworks, Git and a sound understanding for software development processes and paradigms is desirable

How we’ll support you

• Training and development to help you excel in your career.
• Coaching and support from experts in your team.
• A culture of continuous learning to aid progression.
• A range of flexible benefits that you can tailor to suit your needs.

About us and our teams

Please visit our company website for further information:

https://www.db.com/company/company.htm

We strive for a culture in which we are empowered to excel together every day. This includes acting responsibly, thinking commercially, taking initiative and working collaboratively.

Together we share and celebrate the successes of our people. Together we are Deutsche Bank Group.

We welcome applications from all people and promote a positive, fair and inclusive work environment.