Data Scientist - QA Automation
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Barclays Data and Analytics
A career in Data & Analytics at Barclays is a hub for top talent, from beginners to experts, fostering innovation and excellence. You'll drive business strategy, leverage data potential, and enhance the experience of millions of customers in today’s data-driven world.
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Join us as a “Data Scientist - QA Automation”, within the Controls Analytics and Insight team, supporting the Chief Controls Office function.
You may be assessed on 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.
To be successful as a “Data Scientist - QA Automation”, you should have experience with:
Basic/ Essential Qualifications:
Creative solutioning skills – able to formulate and execute a strategy where a playbook may not exist.
Experience in influencing senior stakeholders; you should be as comfortable contributing to a “business meeting” as a technical one. You will be the key person in translating business requirements into technical plans and delivering results back into the business.
Experience training end users in data science and analytics technologies
Experience on Python, SQL, and strong ability to work with large and complex data.
Hands-on experience on supervised/unsupervised models, along with Model design, deployment and optimization.
Desirable skillsets/ good to have:
Recognised degree/ certification in STEM discipline
Experience working with Spark, Alteryx, Tableau/Power BI and Cloud based platform.
Strong verbal and written communication skills, and ability to interface with Business and CCO teams.
This role will be based out of Gurugram.
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.