Data Scientist


Our Mission Statement:

The GBM Big Data Ecosystem solves complex problems using cutting edge technologies; helping to rapidly implement insights from data that can help drive more informed decision making. We will deploy smart machines to process complex and large sets of data, impossible in the legacy manual mining methods. Data underpins everything we do; from risk & regulatory management, through monetization, to monitoring client behaviour.

Underpinning all of this business insight are data assets; data assets are curated, enriched and protected sets of data – created from global, raw, structured and unstructured sources. Creating reusable data assets, through our Data Factory, allows us to expose, new, actionable insight, back to the business. Our Data Science & Engineering teams are partnering with IT to deliver this ecosystem now. Our GBM Big Data Lake is the single largest aggregation of data ever at HSBC. We have over 300 sources which equates to over 20PTB of data, with a business use case portfolio greater than 110 items. We are also utilising the latest machine learning tools and technologies to solve these hypotheses of today AND tomorrow.

Required Skills:

  • Graduated from a top-tier university in applied mathematics, Statistics, Machine learning, quantitative finance or related scientific area.
  • Deep understanding of the theoretical grounds of machine learning and of the corresponding mathematical framework. Strong statistical knowledge is an asset for this position. This includes but not limited to: Naïve Bayes, Linear Algebra, Wider Ensemble Models, Random Forest, Probability Statistics, Neural Networks, Geometric Objects, Multivariate Calculus
  • Good programming skills (Python, R), with exposure to Data Science/Visualisation packages such as SciKit Learn, Keras/Tensorflow, Plotly
  • Preferably an understanding of distributed compute environments e.g. MapReduce, Hadoop, Spark, H2O
  • Good understanding of global markets, markets macrostructure and macro economics
  • Critical mind and ability to challenge, intuition of implicit assumptions in mathematical developments.
  • Good communication skills (written and spoken), ability to engage with different sakeholders and to synthesize.
  • Ability to conduct several related projects simultaneously and to meet tight deadlines.

Principal Accountabilities:

In addition to advocating the benefits of using data for business insights and transforming attitudes towards using data. Therefore, driving better business decisions within GBM, objectives 1-4 are the key responsibility of this new role which consists of implementing four types of analytical principles for prioritized business challenges;

  1. Descriptive
  2. Diagnostic
  3. Predictive
  4. Prescriptive

Role Summary:

This advertised role is key in the overall Data Science team with a reporting line to the Head of Business Insight and Analytics. It is therefore essential that the incumbent has a broad view of the latest and most effective tools and techniques to execute scalable actionable insights as well as a good understanding of the Hadoop ecosystem architecture.

The main focus of the role is on the provision of advice and guidance on implementation of the Data Science Strategy and on implementation of prioritized business challenges using one or more analytics principles. Fundamental to the success of this role will be the ability to deliver business benefit through build and change analytic solutions / models / dashboards / reports within the Data Science Operating Model.

Typical KPIs and Targets:

  • Compelling presentations that result in raising business benefits through data science
  • Quality of work that demonstrate a clear understanding and impact of regulatory demand on data assets
  • Quality of work that demonstrate a clear understanding of business data change
  • Ease of implementation of the Data Analytics Strategy in accordance with CDO standards
  • Reliability of insights developed though customer usage
  • Understanding and ability to articulate business benefits
  • Engagement via feedback from stakeholders
  • Degree of influence across key stakeholders
  • Recognition of the Data Consulting & Advisory as a centre of excellence

Customers / Stakeholders:

Senior business heads (Markets, Banking, Operations, IT)

Leadership & Teamwork:

  • Motivate and collaborate with the data science team.
  • Establishing and engaging a trusted network of data practitioners/champions across GBM
  • Close collaboration with CDO teams
  • Work across matrix organisation
  • Communicate and educate GBM functions on aspects of data analytics

Operational Effectiveness & Control:

  • Identification and escalation of weaknesses in the accuracy of regulatory reporting
  • Implementation of a risk based approach in the provision of advice and guidance
  • Adherence to project & program life-cycles with appropriate quality gates that enforce best data practice
  • Timely identification and escalation of weaknesses based on reporting
  • Effectiveness of analytics gates across key programs

Major Challenges:

  • Understanding complex regulatory reform across a range of GBM businesses including front-office
  • Articulating complex data issues in a simple way to senior stakeholders
  • Sensitivity to regulators and HSBC’s stance to the deferred prosecution agreement
  • Timely implementation of the Data Science Strategy Framework in tight fiscal circumstances

Management of Risk:

  • Management of data security
  • Identification of weaknesses in regulatory reporting
  • Appropriate controls and discipline for aspects of data change within Projects and Programs
  • Management of controls around data access and processing of high risk information

Observation of Internal Controls:

  • Adherence to ITSR and data security
  • Data Sharing and production controls

Knowledge & Experience / Qualifications:

  • Experience in implementing Data Science techniques including the application of insights
  • Exposure to the financial & banking sector across combinations of client onboarding, risk functions, operations, Client MI, - Regulatory data change, data quality, front office operations
  • Exposure to Global Banking & Markets
  • Exposure to formal Data Science process lifecycles and methodologies (e.g. CRISP-DM, KDD, TDSP)