• Join us in building the intelligence that powers product discovery for millions of shoppers and thousands of merchants across the Middle East. • Lead the design and execution of large-scale personalization models that directly impact the company topline. • Design, train, and deploy recommendations/personalization models leveraging deep learning, sequence models (Transformers, GRU), and boosted trees (XGBoost, LightGBM). • Develop multi-objective ranking that blends engagement, conversion, and merchant value into a single ranking score (value model), using multi-task learning where shared representations help. • Build scalable two-stage retrieval and ranking systems — ANN retrieval (FAISS, ScaNN) over user/product/event embeddings feeding learning-to-rank models (pointwise, pairwise, and listwise objectives). • Collaborate with infra to productionize real-time feature pipelines (ClickHouse, Kafka, Spark). • Define serving-time impression and feature logging to eliminate training-serving skew and produce unbiased training data. • Design and run online experiments with rigorous guardrail metrics; correct for position and presentation bias in logged data; apply counterfactual/off-policy evaluation and uplift modeling to attribute lift accurately. • Integrate model outputs with platform APIs for dynamic personalization in search, home feeds, and store pages. • Define best practices for offline evaluation (MAP@K, NDCG) and online experimentation metrics (CTR, CVR, GMV uplift). • Partner with product analytics and data science to iterate on signal enrichment and cold-start strategies. • Mentor junior data scientists and define best practices.

Role Description As a Senior Data Scientist at Coalition you will be responsible for measuring, understanding, and helping optimize Coalition’s underwriting. You will perform statistical analysis to provide data-driven insights. You will help us understand and improve our cyber risk selection and reduction, pricing and automation in order to grow our revenue in a safe and efficient manner. - Analyze diverse datasets including claims data, cybersecurity risk signals, and underwriting databases to extract meaningful patterns and insights. - Large scale data analysis with the objective of producing valuable risk signals to be used for underwriting or risk evaluation of organizations. - Develop and refine statistical and machine learning models to assess cybersecurity risks with applications to underwriting and pricing. - Create comprehensive reports on underwriting efficiency metrics and risk selection quality to inform strategic decisions. - Apply statistical techniques to evaluate and improve our cyber risk assessment methodology. - Provide analytical support for underwriting strategies to balance growth with risk management. - Identify, analyze, and implement opportunities to automate underwriting workflows using advanced analytics and data-driven strategies. - Collaborate cross-functionally with actuarial, product, and engineering teams to implement data-driven improvements. Lead cross-functional initiatives in area of expertise. - Serve as both a technical lead and a mentor within the underwriting team. - Work alongside other data scientists, software engineers, and cyber security engineers to improve underwriting at Coalition. Intellectual curiosity and proactive approach to identifying improvement opportunities. Qualifications - Bachelor's degree in a quantitative field (e.g., Statistics, Mathematics, Computer Science, Actuarial Science, or related discipline). - 5+ years of experience in underwriting, quantitative analysis, or risk modeling in the insurance industry. - Knowledge of underwriting, pricing, reserving, or risk management processes in insurance. - Advanced SQL skills for querying complex databases and joining disparate data sources. - Expertise in data manipulation and analysis using Python, R, or similar analytical tools. - Experience with data visualization tools (e.g., Tableau, Power BI, Looker). - Experience with dashboard creation for operational metrics tracking. - Strong communication and analytical skills. - Capable of working with remote teams. Requirements - Actuarial qualification (nice-to-have). - Experience in cyber insurance or cyber security (nice-to-have). - Prior experience or willingness to learn how to work with large datasets and big data technologies (Snowflake, AWS Athena) (nice-to-have). Benefits - 100% medical, dental, and vision coverage. - Flexible PTO. - Annual home office stipend and WeWork access. - Mental & physical health wellness programs like Headspace, Lumino, and more! - Competitive compensation and opportunity for advancement.

Role Description Join us in building the intelligence that powers product discovery for millions of shoppers and thousands of merchants across the Middle East. As a Senior Data Scientist for the Recommendation Systems Pod, you'll lead the design and execution of large-scale personalization models that directly impact the company topline. This is a rare opportunity to shape the next generation of commerce AI in a high-growth market characterized by highly diverse user and merchant behaviors across the GCC. - Design, train, and deploy recommendations/personalization models leveraging deep learning, sequence models (Transformers, GRU), and boosted trees (XGBoost, LightGBM). - Develop multi-objective ranking that blends engagement, conversion, and merchant value into a single ranking score (value model), using multi-task learning where shared representations help. - Build scalable two-stage retrieval and ranking systems — ANN retrieval (FAISS, ScaNN) over user/product/event embeddings feeding learning-to-rank models (pointwise, pairwise, and listwise objectives). - Collaborate with infra to productionize real-time feature pipelines (ClickHouse, Kafka, Spark). - Define serving-time impression and feature logging to eliminate training-serving skew and produce unbiased training data. - Design and run online experiments with rigorous guardrail metrics; correct for position and presentation bias in logged data; apply counterfactual/off-policy evaluation and uplift modeling to attribute lift accurately. - Integrate model outputs with platform APIs for dynamic personalization in search, home feeds, and store pages. - Define best practices for offline evaluation (MAP@K, NDCG) and online experimentation metrics (CTR, CVR, GMV uplift). - Partner with product analytics and data science to iterate on signal enrichment and cold-start strategies. - Mentor junior data scientists and define best practices. Qualifications - Bachelor's or Master's degree in Computer Science, Machine Learning, or a related technical field. - 4+ years of hands-on ML experience, including 2+ years designing or deploying large-scale recommendation systems. - Track record: Built or maintained systems serving 1M+ users or generating 100M+ personalized predictions daily. - Deep expertise in representation learning, embeddings, attention mechanisms, and multi-task learning. - Demonstrated success integrating multi-stage ranking systems across e-commerce surfaces (search, feeds, product detail pages) with measurable online lift (CVR, GMV). - Proficient with large-scale data ecosystems: Kafka, Spark, ClickHouse, BigQuery, or equivalent. - Strong command of experimentation rigor: guardrail metrics, position-bias correction, off-policy/counterfactual evaluation, and model monitoring. - Skilled in debugging, optimization, and productionization of ML pipelines in cloud or containerized environments.

• Develop analyses to identify patterns, trends, and correlations among indicators • Implement mechanisms for automated generation of insights for governance • Explore the use of AI / LLM models for interpreting and explaining data • Develop a conversational assistant solution for querying and analyzing information • Support the evolution of the governance model toward a data-driven approach