Senior Data Scientist II - Personalization
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Key skills for this role
About the Role
Careem is looking for a Senior Data Scientist II to join the Personalization team, driving real-time cross-vertical personalization across Food, Quik, and Shops. You will own hyper-personalization use cases, advance graph-based retrieval, build next-generation ranking models, and pioneer real-time learning systems.
Key Skills for This Role
Responsibilities
- Drive real time, cross vertical personalization across Food, Quik, and Shops
- Advance graph based retrieval methods for recommendations
- Design and evaluate transformer based architectures for sequential and contextual recommendation
- Pioneer real time learning systems that adapt to user behavior within a session
- Identify where personalization signals, models, or infrastructure can be shared across verticals
- Shape how generative AI and LLM based systems augment retrieval and ranking
- Drive exploratory analysis to understand user behavior across verticals
- Implement scalable machine learning algorithms that run in production on large scale data
- Define and track key metrics for personalization initiatives
- Design and run randomized controlled experiments (A/B tests) and communicate findings
- Build and deploy retrieval augmented generation (RAG) systems within the personalization stack
Requirements
- 6 8 years of experience in data mining, predictive modeling, time series analysis, machine learning, and Big Data methodologies
- Advanced degree in a quantitative discipline such as Physics
Full Job Posting
About the Team
- The Personalization team sits within Careem's Data Science organization and owns the AI systems that decide what every user sees, in what order, and why across Food, Quik, and Shops.
- Our mission is to build the hyper personalization layer for the Careem app: real time, cross vertical recommendation and ranking systems that learn from a user's behavior in one vertical and apply that understanding everywhere else they engage with Careem.
What You'll Do
- Drive real time, cross vertical personalization: Own hyper personalization use cases across Food, Quik, and Shops designing systems that learn a user's intent and preferences in real time and transfer that signal across verticals.
- Advance graph based retrieval: Be a technical lead on Careem's exploration of graph based retrieval methods for recommendations including evaluating and building knowledge graph pipelines that power candidate generation and ranking at scale.
- Build next generation ranking models: Design and evaluate transformer based architectures (XFY) for sequential and contextual recommendation moving Careem's ranking and retrieval stack beyond classical ML toward deep, attention based models.
- Pioneer real time learning: Push toward online/streaming learning systems that adapt to user behavior within a session, not just from batch trained models refreshed on a daily cadence.
- Build for cross learning: Identify where personalization signals, models, or infrastructure can be shared across Food, Quik, and Shops rather than rebuilt per vertical reducing duplicate work and compounding the value of every experiment.
- Be part of a 0 to 1 AI transformation for the Careem app from a personalization standpoint shaping how generative AI and LLM based systems augment retrieval and ranking.
- Build a long term vision for how Careem rethinks customer acquisition and engagement strategies, grounded in data driven decision making.
- Drive exploratory analysis to understand user behavior across verticals, identifying new levers to move metrics and building behavioral models that inform product enhancements.
- Shape and influence the ML models and instrumentation that optimize the product experience, surfacing new areas of opportunity and new product directions.
- Provide product leadership through data driven recommendations communicating the state of the business, root causing metric movements, and using experimentation results to influence product and business decisions.
- Implement scalable machine learning algorithms that run in production on large scale data.
- Run exploratory data analysis to better understand user and business phenomena, and to discover untapped areas of growth and optimization.
What You'll Need
- 6 8 years of experience in data mining, predictive modeling, time series analysis, machine learning, and Big Data methodologies, including transformation and cleaning of structured and unstructured data.
- Advanced degree in a quantitative discipline such as Phys
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