Data Scientist

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Role Description

Reporting into the Lead Data Scientist, you will be responsible for the delivery of our work for our clients within the AML Sector to drive increased value from their data assets.  You will have proven superior capability in assessing the underlying business challenges, accurately defining the problem statement and identifying the appropriate analytical methods and solutions by which their data can be transformed into insight and analytic applications. 

  • You will have experience in data management and curation and the latest techniques for developing predictive models, and segmentations.

  • You will have will have proven hands on experience with SQL and Python/R.

  • You will have experience of working with, guiding and mentoring less experienced analysts to support you in your client work.

Responsibilities

  • Undertake the analyses required for clients from the data aggregation, profiling, model development through to the visualisation and communication of results. 

  • Prepare reports in a manner that is clear and comprehensible to internal and external stakeholders. 

  • Identify new product development and automation opportunities that will support the BAML business objectives. 

  • Supporting the Commercial team in pre-sales activities and meetings. 

  • Effectively communicate with the business lines in order to obtain the necessary information and documentation. 

  • Understanding the client’s sector challenges, business objectives and designing the appropriate analysis methodologies and solutions to meet these.

  • Identify key insights.

Main Contacts

  • Reporting to the Head of Data Science.

  • Working with and reporting into a Senior Data Scientist on a project by project basis.

  • Collaborating closely with:

    • Resource Management.

    • Partner Teams.

KPIs

  • Analytics work consistently meets high confidence and quality levels:

    • Accuracy of work.

    • Clarity of delivery.

    • Meets the brief.

  • Adherence to use of workplace tools – timesheets, leave planning, expenses etc.

Location

  • Lithuania

Knowledge and Experience

​Essential

  • Hands-on application of predictive modelling, data mining and data manipulation techniques. 

  • Proven ability to use tools like SQL/SAS/python/R/linux/git/spark/Hadoop. 

  • Specialist in understanding the analytical methods by which data can be used for insight, how this is articulated and delivered to sponsors, and how this can be best leveraged for the business. 

  • Self-starter with strong appreciation of the value of data. 

  • Good business acumen. 

  • Strong communication skills (oral and written), particularly in rendering complex data outcomes to a non-technical audience. 

  • Strong administrative and reporting skills. 

  • Process-oriented, excellent at identifying business areas for improvement. 

  • Good management information visualisation. 

Desirable

  • Clear understanding of the AML and Financial Services sector challenges and objectives to enable the business to develop commercially relevant propositions using appropriate analysis methodologies and solutions. 

  • Analytics experience in Finance/Banking related to AML and Fraud.