Bank of Ireland: Senior Data Scientist within the Advanced Analytics Team

The Bank of Ireland is seeking a team member to help maximise value from the Bank’s data assets, work in partnership with our external analytic partners, and develop new business opportunities and to improve efficiency.

Bank of IrelandCompany: Bank of Ireland
Location: Dublin, Ireland
Position: Senior Data Scientist within the Advanced Analytics Team

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The Bank of Ireland Marketing & Customer Analytics team focuses on providing customer and marketing insights, customer segmentation, predictive modelling, marketing database management and lead generation campaigns to stakeholders across the Group.

The Opportunity

Would you like to be a member of a team maximising value from the Bank’s data assets, working in partnership with our external analytic partners, to develop new business opportunities and to improve efficiency. The Advanced Analytics team, develop prioritisation and reporting systems enabling the team to maximise a tangible bottom line impact. Leading advanced analytics projects and managing key stakeholders by translating business questions into structured analysis will be fundamental. This is a high visibility role within the team and a great opportunity to make a difference through advanced analytics.

The Person

You have experience in supporting the development of key strategic projects and delivering effective enhancements (Automation, Real time Analytics, Machine Learning, Data Visualisation Text mining and Natural Language Processing). In addition, you are an effective listener who makes sure that Data science doesn't happen in a vacuum. Your exceptional collaboration skills will allow you to deliver in a matrix organisation environment. A third level degree is a pre-requisite, preferably in Maths, Physics, Statistics, computer sciences, econometrics or a related field.

Key Responsibilities

  • Communicate complex concepts and results to key stakeholders not familiar with detailed econometric modelling/data mining and machine learning
  • Formulate insightful conclusions, provide strategic recommendations, and inform management actions
  • Develop processes and methodology to insure that we put the right engagement in front of the right customer at the right time
  • Leverage external data sources to support cross sales and customer acquisition and retention effort.
  • Use best practice in data sciences to provide automated output using both structured and unstructured data, internal and external data
  • Help increase the team knowledge and skills via peer training and mentoring
  • Support and lead the implementation of solutions to deal with capacity constraints (self-service cube, Prioritization and Stakeholder management, etc…)
  • Identify and address productivity bottlenecks and implement automated solution (SAS Macros, VBA, Java, Python, R, Revolution R, Scala, Pig, Spark, Flink, Other Deep Learning tools, other Big Data/Hadoop/HDF-based tools)

Essential Key Requirements

  • 3rd Level Degree or equivalent is essential(preferably in Mathematics, Statistics, Computer Sciences, Physics, Economicsor related discipline).
  • Minimum 4/5 years’ experience
  • Lead by example and encourage the use of best practice throughout the team. Allocate and prioritize the team’s work where appropriate
  • Rigorous attention to detail and accuracy with the ability to produce accurate quality information within agreed timeframes
  • Strong numeric and analytical skills, with ability to identify causal relationships between multiple, widely varied data fields
  • Advanced Programing skills
  • Advanced Data manipulations skills (SQL, NoSQL, Fuzzy matching, Web Scraping, Text Mining, Spark, Flink)
  • Excellent interpersonal and communication skills
  • Ability to work independently with minimum supervision

Desirable Key Requirements

  • Knowledge of statistics/Machine learning for Marketing ideally in a Financial Services environment, Telcos, FMCG or Internet companies
  • Knowledge of advanced analytical algorithm: Logistic regression, clustering, propensity modelling, Random Forest, K-Means, SVM, Gradient Boosting
  • Proficient using data cleaning tools (e.g. Data Wrangler, OpenRefine)