To be successful in this role, you should have below skills:
- Undergraduate or equivalent degree. Strong preference is for a degree in a numerical discipline.
- Subscribes to and can demonstrate Barclays Values and Mindset.
- Proficiency in scripting languages (e.g., Python, R) and ML frameworks/tools (e.g., Scikit-learn, TensorFlow).
- Proficiency in SQL for querying multiple databases and rapid prototyping to quickly iterate and test automation solutions.
- Deep understanding of machine learning and data mining principles, tools, and processes.
- Strong communication skills, displaying an ability to communicate complex ideas to a diverse audience across all areas and grade.
Some other highly valued skills may include below:
- Demonstrable understanding of technology and/or business processes.
- Expertise in cloud technologies, particularly AWS, and familiarity with Gen AI implementation will be preferred.
- Experience with DevOps tools like Git/Bitbucket will be preferred.
- Experience with workflow tools like Power Automate, Alteryx will be preferred.
- Intellectual honesty and curiosity.
- An ‘influencer’ and negotiator. Able to understand the challenges facing Treasury and influence positive outcomes.
You may be assessed on the key critical skills relevant for success in role, such as risk and controls, change and transformation, business acumen strategic thinking and digital and technology, as well as job-specific technical skills.
This role is based in our Chennai office.
Purpose of the role
To use innovative data analytics and machine learning techniques to extract valuable insights from the bank's data reserves, leveraging these insights to inform strategic decision-making, improve operational efficiency, and drive innovation across the organisation.
Accountabilities
- Identification, collection, extraction of data from various sources, including internal and external sources.
- Performing data cleaning, wrangling, and transformation to ensure its quality and suitability for analysis.
- Development and maintenance of efficient data pipelines for automated data acquisition and processing.
- Design and conduct of statistical and machine learning models to analyse patterns, trends, and relationships in the data.
- Development and implementation of predictive models to forecast future outcomes and identify potential risks and opportunities.
- Collaborate with business stakeholders to seek out opportunities to add value from data through Data Science.