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Job Overview:
Apply data science and machine learning techniques to drive business decisions and solve complex business problems. Focus on extracting insights from granular data, and leveraging statistical models and state-of-the-art machine learning techniques to shape the future of what data-driven organizations look like.
Responsibilities
Apply data science and machine learning techniques to projects and business-relevant analysis and research to enable solving complex business problems and driving business decisions using state-of-the-art techniques.
Build data predictive, prescriptive and descriptive analytics models and surface key insight to drive decision making across the organization.
Identify the right models and select the right parameters to solve a problem for statistical models and machine learning models.
Identify, predict and model customer behavior, and convey analytics findings to key business stakeholders.
Identify and integrate new datasets that can be leveraged to address business area needs.
Create repository of analytics models and assets to be leveraged for future usage to support business applications, ensuring system scalability, security, performance, and reliability.
Build and maintain a large-scale analytics infrastructure to be used across the business and consistently conduct research, design, implementation, and validation of cutting-edge algorithms in order to analyze diverse data sources in order to enable desired business outcomes.
Support the implementation of best programming practices and sharing of data science skills and knowledge across the organization.
Advise on transformative business strategies through the measurement, manipulation, reporting and dissemination of broad sets of data.
Prepare analysis and presentations for the Chief Data Scientist and relevant stakeholders to communicate analytics models in business language that will give insights for departmental as well as business-wide decision making.
Keep up to date with the technical developments and their applications in the financial services sector.
Qualifications:
Bachelor’s degree in Data Science, Actuarial Science, Mathematics, Statistics, Computer Science, Computer Engineering, Management of Information and Communication Technology, Business Informatics, or equivalent combination of education and experience.
Master’s degree in Data Science, Data Analytics, Statistics, Applied Mathematics, Computer Science, Computer Engineering, Operations Research, or related discipline is preferred.
Experience working in the financial services sector is preferred
0-2 years’ experience in data science, machine learning, or data analytics role
Proficiency with data mining and statistical analysis
Advanced pattern recognition and predictive modelling experience
Data management experience, including sourcing, cleaning and linking different datasets.
Experience working with granular, messy and/or textual data
Fluency in at least one mainstream programming language (for example, Python, R), and with good SQL skills
Experience with at least one of the following data visualization tools: Tableau, Power BI
A strong ability to communicate analytical outputs to non-technical audiences in a clear but rigorous way, both verbally and in writing