Up to 8 yrs relevant experience beyond first degree.
Experience with common data science toolkits, programming languages, visualisation tools and SQL/NoSQL databases.
Good applied statistical knowledge with emphasis in business and finance related statistical distributions, statistical testing, modeling, regression analysis, etc.
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Build and maintain People metrics, scorecards, and dashboards across the employee lifecycle, covering engagement, productivity, skills, and cost, designed for usability by non-technical audiences and not just analytical accuracy.
Design and ship self-service tools and workflows, including integrations with internal platforms such as Valet and Cortana, that enable managers and leaders to conduct their own people analysis without PSI mediation.
Transform existing People assets (e.g. the People Impact Card) from static summaries into automated, insight-driven outputs with actionable recommendations and just-in-time nudges.
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Design, measure, and recommend A/B tests and multivariate experiments, including layout optimisation, UI/UX efficacy, algorithm effectiveness, and API performance.
Analyse clickstream and transactional data to uncover insights on user behaviour and guide product metrics. You will manage instrumentation for all feature releases within assigned tech families within the bank.
Develop self-serve solutions for partners that are scalable and automated to handle a product environment.
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Develop and execute incrementality measurement studies to isolate true campaign impact, including A/B testing, holdout analysis, and matched market approaches for paid marketing campaigns.
Build and maintain Media Mix Modeling (MMM) frameworks to quantify the impact of paid marketing spend across channels, optimize budget allocation, and forecast campaign performance.
Design and conduct geo-lift studies to measure causal impact of paid marketing initiatives at regional or market levels, supporting strategic decision-making and ROI validation.
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Root-Cause Analysis & Problem Solving: Interact directly with stakeholders to resolve data quality issues using techniques like root-cause analysis, continuously improving trust and confidence in our enterprise data products
Platform Ownership: Utilize and manage enterprise data quality and cataloging platforms (e.g., Informatica DQ, Collibra DQ, Ataccama)
Governance Alignment: Support Data Governance teams in deploying observability practices and ensuring compliance with enterprise data quality and fitness standards
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You will design, train, and fine-tune model architectures, utilizing a toolkit that includes Graph Neural Networks (GNNs), Transformers, fine-tuned open-source LLMs (like Qwen), and Gradient Boosted Trees.
You will lead the end-to-end lifecycle of your models—from production deployment to performance monitoring—iterating alongside software and product engineering teams.
You will leverage Generative AI tools (like coding assistants and analytical co-pilots) in your daily workflows to accelerate code generation, automate testing, and enhance your overall productivity.
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Allocation Optimization Design optimization models for inventory allocation across stores Balance constraints such as stock availability, demand, and capacity
Dashboarding and Analytics Create and maintain impactful analytics that drive business decisions across various platforms (e.g., dashboards)
Data & Collaboration Work with data stakeholders to ensure reliable data pipelines and translate business requirements into analytical solutions
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