Divisional Risk and Control Ma
Deutsche Bank AG
Pune, India
1d ago

Position Overview

Role summary The Production Integrity Risk, Insights & Controls department in Group CIO help the Bank manage it's IT risk profile by ensuring the bank is safe, secure and resilient and course correcting with actionable insights.

We work together with all the Bank CIOs to proactively manage the right IT risks the right way by driving actionable insights from data and improving transparency.

The Actionable Quant Analytics / Data Science Practice designs and owns advanced data led and analytics driven solutions that mine multiple terabytes of IT Service Management / Technology operations data points to derive enterprise actionable insights, enabling teams to make the right decisions at the right time.

The first such product put into production in 2018 after successful PoC in 2017 is the IT Stability Risk Score Model that continuously ranks all of the Bank's 4000+ IT Production applications, leveraging a set of 63 dynamic metrics that are used to predict the likelihood and impact of incidents that will affect each application in the next 4 weeks, helping quantitatively track the stability health of each application as well as prioritise high value remediation activities.

This role will support this vision, working hands-on as a team member in a variety of tactical and strategic quant analytics data science projects and initiatives reporting directly to the Risk, Insights & Controls Actionable Quant Analytics / Data Science Practice Director.

This is a hands on data scientist role, involving working with diverse and large amounts of structured and unstructured data on a regular basis, helping the business derive actionable insights and answers to complex questions related to what drives technology risk in production applications across the Bank and how it can best be managed.

As such it is not solely a back office technical data programing role, as it also requires regular direct discussions and involvement with customer / business / IT process owners to understand their challenges and how those translate in to data and analytical approaches that will help address them in a practical way. Role responsibilities

  • Support Risk, Insights & Controls Actionable Quant Analytics / Data Science Practice in engaging with various business stakeholders, decision makers, process owners, technology
  • Help translate those business requirements into pragmatic and detailed data and analysis approaches and plans that will meet the desired objectives / outcomes
  • Using data science and exploratory analysis techniques to identify and capitalise on opportunities for cost reduction, revenue enhancement, operational enhancement, business optimization associated with the 3 following areas : o IT Production Application Stability : help determine, quantify and simulate the various drivers of IT Production application stability, in particular helping evolve the current set of predictive models by leveraging further sets of SDLC (software Development Life Cycle) data as well as application monitoring data and unstructured data related to ITSM events (change, incident and problem tickets text fields, priv access sessions key stroke logs).
  • In particular, the initial focus will be on helping put into production a new predictive model quantitatively predicting the likelihood of an IT application or lower level infrastructure or shared platform asset to cause one or more incidents impacting a Production application.

    o IT Production Application Compliance : working with the Integrated Control Framework GRC (Governance Risk & Compliance) to understand drivers of IT controls coverage, adoption and compliance testing, to align with the firm IT practices, processes and policies and regulatory requirements o IT Production Support Effectiveness : work with ITSM SMEs, external data science specialist consultants and the CIO production support team to develop quantitative methods and ratings to assess the effectiveness of production support operations, helping identify waste / areas of work that could be eliminated (e.

    g. avoidable repeat work), optimize work (e.g. routing tickets to best handler to reduce work effort and time to resolution) and reduce overall costs by strategically helping align skills to work from location perspective (best shoring).

  • Help the project team manage the end to end analytics development lifecycle (from ideation to model validation and deployment) of the data science solutions being developed, working the platform data engineering teams from an implementation perspective, to ensure safe ongoing delivery of high value, business relevant solutions to programme stakeholders
  • Contribute to the design, development and deployment of data visualisation solutions that will provide insights into business problems in scope
  • Ensure all deliverables and solutions are of an extremely high level of quality, all testing is fully documented and signed-
  • off, and providing quality assurance and model validation support to other team members

  • Utilise data science, statistical modelling and machine learning techniques to achieve organisational and project objectives Experience required
  • Educated to Bachelor's degree level or equivalent qualification / work experience in a numeric / quantitative scientific field essential (ideally MSc or PhD)
  • Excellent English speaking and writing skills excellent listening and communications skills
  • Proven hands on track record / experience (ideally 10 years+, minimum 5years) in developing pragmatic and innovative data science solutions, preferably using open source technology stacks and working in agile development environment and teams
  • Proven experience with R language and environment for statistical computing and graphics (and associated data science packages) and Python Pandas / scikit-
  • learn is an absolute essential other data science programming platform / languages would also be beneficial but not essential

  • Experience with Extract, Transform and Load (ETL) processes and data processing and management language such as SQL is essential Essential Skills / Capabilities required Communication :
  • Being able to communicate with multiple stakeholders using data is a key attribute as nothing in technology today is performed in a vacuum;
  • there's always some integration between systems, applications, data and people.

  • Storytelling' ability through data e.g. translates what is a mathematical result into an actionable insight or intervention.
  • Adept at telling a story to each of the business, technology, and data stakeholders.

  • Ability to distill challenging technical information into a form that is complete, accurate, and easy to present to ensure that the audience understands and therefore results will be used to support directional action by the business Intellectual curiosity and Business acumen :
  • Desire to acquire more knowledge. Need to be able to ask questions about data as around 80 percent of your time will be spent discovering and preparing data.
  • Also you will have to be willing to continuously learn to keep up with the pace.

  • Need of a solid understanding of the Information Technology in Financial Services and ability to quickly grasp what business problems are looking to being solved.
  • Being able to discern which problems are important to solve for the business, in addition to identifying new ways the business should be leveraging its data.
  • Understand how the problem to be solved can impact the business. Hence the need to know about how businesses operates so your efforts can be directed in the right direction. Critical thinking :
  • Evidence of ability to apply objective analysis of facts on a given topic or problem before formulating opinions or rendering judgments.
  • Need to understand the business problem or decision being made and be able to 'model' or 'abstract' what is critical to solving the problem, versus what is extraneous and can be ignored.

  • Evidence of ability to suspend belief, e.g. knowing what to expect when working in any area, but also knowing that experience and intuition are imperfect, as experience provides benefits but is not without risk if we get too complacent.
  • Ability to step back and being able to assess a problem or situation from multiple points of view. Coding :

  • Proven knowledge as to how to write code and comfortable handling a variety of programming tasks, in particular R and Python
  • Programming skills need to comprise both : o computational aspects (dealing with large volumes of data, working with real-
  • time data, cloud computing, unstructured data), as well as o statistical aspects (working with statistical models like regression, optimization, clustering, decision trees, random forests, etc.) Mathematics :

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