Data Scientist

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Job Role - Data Scientist

Duration - 3-6 Months

Location - London

Purpose of Department and Purpose of Role:

- Advanced Analytics (AA) team is involved in design, implementation and performance monitoring of predictive analytics tools and is based within Group Risk Analytics, Regulatory Compliance (GRA RC) function. Members of the AA team are involved in designing end-to-end predictive analytics solution, develop data platforms, build Machine Learning models (supervised, unsupervised, reinforced and evolutionary algos) and create software products to deploy these solutions within Regulatory Compliance frame work. S/he will contribute in developing advanced analytics strategy and its execution to ensure key business performance targets are achieved. S/he will engage with various stakeholders within wider GRA function, lead various PoC, execute the development plan, deploy the solution and manage the change upon implementation. These areas of engagement are utilised in the bank's risk management activities across various market conduct activities, including within insider dealing, market manipulation, and unfair wholesale customer treatment. The team's output forms the information basis for strategic planning, RC model risk management and broader Bank-wide RC activities and enables effective decision making to satisfy business needs and regulatory requirements, as well as assists the bank with addressing unforeseen challenges and new risk drivers.

- This role supports various analytical and highly quantitative projects within GRA RC function. The analytics team is involved in providing best-in-class Analytics solutions to the Regulatory Compliance (RC) function across lines of businesses and geographies. The team caters to both strategy and operations aspect of managing regulatory compliance through a combination of the following activities

- Enable Bank to monitor their transaction activity and generate automated alerts for activities that may be unusual and non-compliant with regulatory requirements and internal standards.

- Design, deploy, monitor and maintain models in various scenarios for various market conduct surveillance activities

- Support RC teams globally with analytical solutions for increased efficiency and operational effectiveness through models, scorecards and data attestation frameworks

- Define Compliance Metrics and Design / Deploy predictive MI dashboards to provide insight into system / process performance

- Support bank-wide Model Risk Governance framework

- Support proof-of-concept (PoC) and proof-of-value (PoV) evaluations

Job Description:

The incumbent for this role is expected to:

* Participate in projects leading to solutions using various analytical techniques for the Market Conduct predictive analytics activities through involvement in any or all of the following activities

- Design of predictive analytics strategies and models, their prototyping and implementation

- Develop advanced machine learning capabilities to codify human decisioning

- Data Quality Analysis, Segmentation , Threshold Setting and Tuning for Surveillance Monitoring

- Trend Analysis and Dashboard Creation based on Visualization techniques

- Data Implementation Checks and Data Quality Assurance

- Development of model and data attestation frameworks

- Development of bespoke solutions and tools to enhance the effectiveness and efficiency of surveillance within Regulatory Compliance

* Execute the assigned projects/ analysis as per the agreed timelines and with accuracy and quality.

* Complete analysis as required and document results and formally present findings to management

* Adhere with all the applicable compliance policies (Data security policy, operational risk, Functional manual, Group Compliance Manual, Audit recommendations, Internal Control requirements, relevant regulatory guidelines etc.) and the bank's business standards as applicable.

* Lead projects and initiatives including supervision and mentoring of team members

Skills/Experience Required:

* Creative and analytical thought process and aptitude for problem solving;

* Post Graduate qualifications (quantitative MSc / PhD) in Statistics, Applied Mathematics, Econometrics, Electronic Engineering or any other relevant quantitative discipline;

* At least 10 years of post-qualification experience in Machine Learning. Knowledge and understanding of financial services preferred and market conduct landscape is preferred;

* Knowledge of statistical and numerical techniques, data manipulation, feature engineering, linear and non-linear regressions, predictive modelling, machine learning (NN, CNN, SVM, Random Forest, Naïve Bayes, KNN, K-means and other Gradient Boosting algos, Image recognition, NLP, sentiment analysis, text abstraction), supervised, un-supervised, reinforced learning and evolutionary algorithms, sentiment analysis and text abstraction tools;

* Strong experience in any of the following: Matlab, Python, R with relevant ML libraries;

* Strong knowledge of Big Data stack such as Hadoop or Hortonworks, NoSQL (Elastic, MongoDB, HBase, Cassandra), Data ingestion tools (Flume, Kafka, Sqoop, Storm)

* Project based business knowledge, and awareness of model risk governance;