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
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).
off, and providing quality assurance and model validation support to other team members
learn is an absolute essential other data science programming platform / languages would also be beneficial but not essential
there's always some integration between systems, applications, data and people.
Adept at telling a story to each of the business, technology, and data stakeholders.
Also you will have to be willing to continuously learn to keep up with the pace.
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.
Ability to step back and being able to assess a problem or situation from multiple points of view. Coding :
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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