Jobs at Delivery Hero

Role Summary: The Growth Data Science Director will play a pivotal role in leading and accelerating growth strategies across multiple dimensions including marketing optimization and efficiency growth product initiatives (e.g. churn reduction) ecosystem plays and ads revenue optimization for tmart and Quick commerce. This leader will drive business impact through the design execution and interpretation of experiments empowering product marketing and operations teams to make data-informed decisions. Whats On Your Plate Data Science • Responsible for defining and executing a comprehensive data strategy that aligns with the companys goals leveraging data to drive business decisions and achieve strategic objectives. • Lead cross-functional teams to design and implement innovative data-driven products and analytics insights to deliver measurable impact on business outcomes. Experimentation & Causal Inference • Develop and oversee frameworks for rigorous A/B testing multi-armed bandit testing and quasi-experimental designs. • Establish best practices for hypothesis formulation testing methodologies and causality analysis to ensure valid and actionable insights. • Drive adoption of experimentation as a core decision-making process across the company. Analytics & Insights • Partner with cross-functional stakeholders to translate business challenges into well-defined analytical problems. • Present clear data-driven recommendations to senior leadership and executive teams. • Ensure statistical rigor and best practices in all analyses conducted by the data science team. Strategic Leadership and Business Acumen • Demonstrate strategic leadership skills and an understanding of how data science can drive business success. • Articulate your vision for your area and align your teams work with broader organizational goals. • Identify emerging trends and technologies that can drive value for the business and take steps to incorporate them into your teams work. Collaboration and Influence • Work collaboratively with a wider range of stakeholders including senior executives product managers and engineers. • Build strong partnerships across the organization communicate effectively and influence decision-making. • Manage relationships with external partners such as vendors or academic institutions and represent the organization in industry events and conferences. Resource Management As a director you will have responsibility for managing a larger team of data scientists analysts and engineers as well as budgets and resources. • Make data-driven decisions about resource allocation prioritize projects and initiatives and manage risk effectively. • Coach and mentor other managers within your team helping them to develop their skills and grow as leaders. Strategic Hiring and Talent Development • Responsible for recruiting and retaining top talent within your organization. • Work with HR and recruiting teams to develop job descriptions and identify candidates as well as creating onboarding and training programs to help new hires get up to speed quickly. • Develop career paths for your team members set expectations for their growth and development and provide regular feedback and coaching to help them achieve their goals. Organizational Change Management • Manage organizational change effectively whether it involves new technologies processes or initiatives. • Communicate the rationale for change involve key stakeholders in decision-making and manage resistance effectively. • Develop and implement change management plans that minimize disruption and ensure that your team is able to adapt to new ways of working. Qualifications : What Did We Order • A minimum of 8-10 years of professional experience in data science including leadership roles in data science teams or consulting. • A Ph.D. or Masters degree in a relevant field such as computer science statistics data science or a related disciplines • Proven experience in leading and managing managers and a demonstrated ability to foster a high-performance team culture. • Must possess a deep and practical knowledge of data science encompassing statistics causality experimentation and causal inference as well as knowledge in modeling. • Must have a track record of applying these skills and leading teams to design and implement innovative data-driven products and deliver impactful insights and recommendations Business Acumen: • Proven ability to understand and navigate complex business landscapes including familiarity with industry-specific challenges and opportunities. • Demonstrated experience in identifying and quantifying the business impact of data-driven initiatives such as revenue growth cost savings and customer retention. Strategic Thinking: • A strategic mindset with the ability to envision and communicate long-term data-driven goals and objectives that align with the organizations overall strategy. • Proficiency in developing and executing data-driven roadmaps and plans that drive innovation competitive advantage and market growth. Remote Work : No Employment Type : Full-time Key Skills Laboratory Experience,Immunoassays,Machine Learning,Biochemistry,Assays,Research Experience,Spectroscopy,Research & Development,cGMP,Cell Culture,Molecular Biology,Data Analysis Skills Experience: years Vacancy: 1

As the leading delivery company in the region we have a great responsibility and opportunity to impact the lives of millions of customers restaurant partners and riders. To realize our potential we need to advance our platform to become much more intelligent in how it understands and serves our users. We are looking for an Sr. AI Engineer to join our GenAI Squad your mission will be to improve the quality of the decisions made across product and business via relevant reliable and actionable data. You will own a particular domain across product and business and will work closely with the corresponding product and business managers as part of a talented team of data scientists and data engineers. You will own the entire data value chain including logging data modeling analysis reporting and experimentation. Many of our initiatives will focus on leveraging Generative AI for tasks such as data enrichment smart content understanding and automated decision-making to enhance user experiences and business operations. Responsibilities • Leveraging ambiguous business problems as opportunities to drive objective criteria using data. • Solving complex business problems using the simplest most appropriate algorithms to deliver business value. • Designing and implementing effective and impactful machine learning and generative AI systems in production. • Developing a deep understanding of the product experiences and business processes that make up your area of focus. • Developing a deep familiarity with the source data and its generating systems through documentation interacting with the engineering teams and systematic data profiling. • Contributing heavily to the design and maintenance of the data models that allow us to measure performance and comprehend performance drivers for your area of focus. • Working closely with product and business teams to identify important questions that can be answered effectively with data. • Delivering well-formed relevant reliable and actionable insights and recommendations to support data-driven decision-making through deep analysis and automated reports. • Designing planning and analyzing experiments (A/B and multivariate tests). • Supporting product and business managers with KPI design and goal setting. • Mentoring other data scientists in their growth journeys. • Contributing to improving our ways of working our tooling and our internal training programs. Qualifications : Requirements Technical Experience • Experience in machine learning generative AI deep learning recommendation systems pattern recognition data mining and artificial intelligence. • Deep knowledge and experience in ML and GenAI frameworks (e.g. Scikit-learn XGBoost LightGBM CatBoost SVMs Keras TensorFlow PyTorch Hugging Face Transformers LLM fine-tuning). • Excellent SQL. • Competence with reproducible data analysis using Python or R. • Familiarity with data modeling and dimensional design. • Strong command over the entire data lifecycle including problem formulation data auditing rigorous analysis interpretation recommendations and presentation. • Familiarity with different types of analysis including descriptive exploratory inferential causal and predictive analysis. • Deep understanding of various experiment design and analysis workflows and the corresponding statistical techniques. • Familiarity with product data (impressions events etc.) and product health measurement (conversion engagement retention etc.). • Experience with LLMs and NLP-based solutions for data enrichment and smart automation is a plus. • Familiarity with BigQuery and the Google Cloud Platform is a plus. • Data engineering and data pipeline development experience (e.g. via Airflow) is a plus. Qualifications • Bachelors degree in engineering computer science technology or similar fields. A postgraduate degree is a plus but not required. • 5 years of experience working in data science machine learning and Gen AI. • Experience doing data science in an online consumer product setting is a plus. • A good problem solver with a figure it out growth mindset. • An excellent collaborator and communicator. • A strong sense of ownership and accountability. • A keep it simple approach to #makeithappen. Remote Work : No Employment Type : Full-time Key Skills ASP.NET,Health Education,Fashion Designing,Fiber,Investigation Experience: years Vacancy: 1

Role Summary: The Growth Data Science Director will play a pivotal role in leading and accelerating growth strategies across multiple dimensions including marketing optimization and efficiency growth product initiatives (e.g. churn reduction) ecosystem plays and ads revenue optimization for tmart and Quick commerce. This leader will drive business impact through the design execution and interpretation of experiments empowering product marketing and operations teams to make data-informed decisions. Whats On Your Plate Data Science • Responsible for defining and executing a comprehensive data strategy that aligns with the companys goals leveraging data to drive business decisions and achieve strategic objectives. • Lead cross-functional teams to design and implement innovative data-driven products and analytics insights to deliver measurable impact on business outcomes. Experimentation & Causal Inference • Develop and oversee frameworks for rigorous A/B testing multi-armed bandit testing and quasi-experimental designs. • Establish best practices for hypothesis formulation testing methodologies and causality analysis to ensure valid and actionable insights. • Drive adoption of experimentation as a core decision-making process across the company. Analytics & Insights • Partner with cross-functional stakeholders to translate business challenges into well-defined analytical problems. • Present clear data-driven recommendations to senior leadership and executive teams. • Ensure statistical rigor and best practices in all analyses conducted by the data science team. Strategic Leadership and Business Acumen • Demonstrate strategic leadership skills and an understanding of how data science can drive business success. • Articulate your vision for your area and align your teams work with broader organizational goals. • Identify emerging trends and technologies that can drive value for the business and take steps to incorporate them into your teams work. Collaboration and Influence • Work collaboratively with a wider range of stakeholders including senior executives product managers and engineers. • Build strong partnerships across the organization communicate effectively and influence decision-making. • Manage relationships with external partners such as vendors or academic institutions and represent the organization in industry events and conferences. Resource Management As a director you will have responsibility for managing a larger team of data scientists analysts and engineers as well as budgets and resources. • Make data-driven decisions about resource allocation prioritize projects and initiatives and manage risk effectively. • Coach and mentor other managers within your team helping them to develop their skills and grow as leaders. Strategic Hiring and Talent Development • Responsible for recruiting and retaining top talent within your organization. • Work with HR and recruiting teams to develop job descriptions and identify candidates as well as creating onboarding and training programs to help new hires get up to speed quickly. • Develop career paths for your team members set expectations for their growth and development and provide regular feedback and coaching to help them achieve their goals. Organizational Change Management • Manage organizational change effectively whether it involves new technologies processes or initiatives. • Communicate the rationale for change involve key stakeholders in decision-making and manage resistance effectively. • Develop and implement change management plans that minimize disruption and ensure that your team is able to adapt to new ways of working. Qualifications : What Did We Order • A minimum of 8-10 years of professional experience in data science including leadership roles in data science teams or consulting. • A Ph.D. or Masters degree in a relevant field such as computer science statistics data science or a related disciplines • Proven experience in leading and managing managers and a demonstrated ability to foster a high-performance team culture. • Must possess a deep and practical knowledge of data science encompassing statistics causality experimentation and causal inference as well as knowledge in modeling. • Must have a track record of applying these skills and leading teams to design and implement innovative data-driven products and deliver impactful insights and recommendations Business Acumen: • Proven ability to understand and navigate complex business landscapes including familiarity with industry-specific challenges and opportunities. • Demonstrated experience in identifying and quantifying the business impact of data-driven initiatives such as revenue growth cost savings and customer retention. Strategic Thinking: • A strategic mindset with the ability to envision and communicate long-term data-driven goals and objectives that align with the organizations overall strategy. • Proficiency in developing and executing data-driven roadmaps and plans that drive innovation competitive advantage and market growth. Remote Work : No Employment Type : Full-time Key Skills Laboratory Experience,Immunoassays,Machine Learning,Biochemistry,Assays,Research Experience,Spectroscopy,Research & Development,cGMP,Cell Culture,Molecular Biology,Data Analysis Skills Experience: years Vacancy: 1

Summary As the leading delivery platform in the region we have a unique responsibility and opportunity to positively impact millions of customers restaurant partners and riders. To achieve our mission we must scale and continuously evolve our machine learning capabilities including cuttingedge Generative AI (genAI) initiatives. This demands robust efficient and scalable ML platforms that empower our teams to rapidly develop deploy and operate intelligent systems. As an ML Platform Engineer your mission is to design build and enhance the infrastructure and tooling that accelerates the development deployment and monitoring of traditional ML and genAI models at scale. Youll collaborate closely with data scientists ML engineers genAI specialists and product teams to deliver seamless ML workflowsfrom experimentation to production servingensuring operational excellence across our ML and genAI systems. Qualifications : Responsibilities • Design build and maintain scalable reusable and reliable ML platforms and tooling that support the entire ML lifecycle including data ingestion model training evaluation deployment and monitoring for both traditional and generative AI models. • Develop standardized ML workflows and templates using MLflow and other platforms enabling rapid experimentation and deployment cycles. • Implement robust CI/CD pipelines Docker containerization model registries and experiment tracking to support reproducibility scalability and governance in ML and genAI. • Collaborate closely with genAI experts to integrate and optimize genAI technologies including transformers embeddings vector databases (e.g. Pinecone Redis Weaviate) and realtime retrievalaugmented generation (RAG) systems. • Automate and streamline ML and genAI model training inference deployment and versioning workflows ensuring consistency reliability and adherence to industry best practices. • Ensure reliability observability and scalability of production ML and genAI workloads by implementing comprehensive monitoring alerting and continuous performance evaluation. • Integrate infrastructure components such as realtime model serving frameworks (e.g. TensorFlow Serving NVIDIA Triton Seldon) Kubernetes orchestration and cloud solutions (AWS/GCP) for robust production environments. • Drive infrastructure optimization for generative AI usecases including efficient inference techniques (batching caching quantization) finetuning prompt management and model updates at scale. • Partner with data engineering product infrastructure and genAI teams to align ML platform initiatives with broader company goals infrastructure strategy and innovation roadmap. • Contribute actively to internal documentation onboarding and training programs promoting platform adoption and continuous improvement. Requirements Technical Experience • Strong software engineering background with experience in building distributed systems or platforms designed for machine learning and AI workloads. • Expertlevel proficiency in Python and familiarity with ML frameworks (TensorFlow PyTorch) infrastructure tooling (MLflow Kubeflow Ray) and popular APIs (Hugging Face OpenAI LangChain). • Experience implementing modern MLOps practices including model lifecycle management CI/CD Docker Kubernetes model registries and infrastructureascode tools (Terraform Helm). • Demonstrated experience working with cloud infrastructure ideally AWS or GCP including Kubernetes clusters (GKE/EKS) serverless architectures and managed ML services (e.g. Vertex AI SageMaker). • Proven experience with generative AI technologies: transformers embeddings prompt engineering strategies finetuning vs. prompttuning vector databases and retrievalaugmented generation (RAG) systems. • Experience designing and maintaining realtime inference pipelines including integrations with feature stores streaming data platforms (Kafka Kinesis) and observability platforms. • Familiarity with SQL and data warehouse modeling; capable of managing complex data queries joins aggregations and transformations. • Solid understanding of ML monitoring including identifying model drift decay latency optimization cost management and scaling APIbased genAI applications efficiently. Qualifications • Bachelors degree in Computer Science Engineering or a related field; advanced degree is a plus. • 3 years of experience in ML platform engineering ML infrastructure generative AI or closely related roles. • Proven track record of successfully building and operating ML infrastructure at scale ideally supporting generative AI usecases and complex inference scenarios. • Strategic mindset with strong problemsolving skills and effective technical decisionmaking abilities. • Excellent communication and collaboration skills comfortable working crossfunctionally across diverse teams and stakeholders. • Strong sense of ownership accountability pragmatism and proactive bias for action. Remote Work : No Employment Type : Fulltime Key Skills ASP.NET,Health Education,Fashion Designing,Fiber,Investigation Experience: years Vacancy: 1

About the opportunity As the leading delivery company in the region, we have a great responsibility and opportunity to impact the lives of millions of customers, restaurant partners, and riders. To realize our potential, we need to advance our platform to become much more intelligent in how it understands and serves our users. As a Sr. Data Scientist (AI & ML) on the global AI hub, your mission will be to design, build, and ship the machine learning and generative AI systems that power decisions across product and business. You will own a particular domain end to end, working closely with product and business managers as part of a talented team of data scientists and machine learning engineers. You will own the full ML lifecycle, from problem framing, data modeling, and feature engineering through model training, deployment, serving, and monitoring in production. Many of our initiatives will focus on leveraging Generative AI and LLMs for tasks such as data enrichment, smart content understanding, and automated decision-making to enhance user experiences and business operations at scale. Responsibilities • Framing ambiguous business problems as well-defined machine learning and data science problems, with clear, objective success criteria. • Providing high-quality, impactful insights and data-driven recommendations through rigorous analysis and automated reporting to drive strategic organizational choices. • Designing, building, and shipping end-to-end machine learning and generative AI systems in production — spanning data pipelines, feature engineering, model training, serving, and monitoring. • Taking on engineering-heavy work end to end: architecting robust ML-based systems, writing clean and scalable production code, and training, deploying, and maintaining reliable ML models that solve real business problems at scale. • Training, evaluating, and iterating on models — selecting the simplest, most appropriate algorithms and architectures to deliver measurable business value. • Leveraging LLMs and generative AI for data enrichment, smart content understanding, and automated decision-making within production systems. • Building and maintaining the data models, features, and pipelines that power model training and allow us to measure performance and its drivers for your area of focus. • Designing, planning, and analyzing experiments (A/B and multivariate tests) to rigorously measure model and product impact. • Developing deep familiarity with source data and its generating systems through documentation, collaboration with engineering teams, and systematic data profiling. • Partnering with product and business teams to identify high-impact opportunities and translate them into ML solutions and actionable, data-driven recommendations. • Mentoring other data scientists in their growth journeys. • Elevating engineering and ML best practices — improving our ways of working, tooling, MLOps, and internal training programs. What you need to be successful Technical Experience • Deep expertise in machine learning, generative AI, deep learning, recommendation systems, NLP, pattern recognition, data mining. • Deep hands-on knowledge of ML and GenAI frameworks (e.g. Scikit-learn, XGBoost, LightGBM, CatBoost, SVMs, Keras, TensorFlow, PyTorch, Transformers, LLM fine-tuning). • Strong software engineering fundamentals: excellent coding skills, a solid grasp of data structures and algorithms, and proven ability in both general system design and ML system design. • Proven experience building, deploying, serving, and monitoring ML models in production, with a strong grasp of MLOps practices. • Strong data and ML engineering skills, including building and orchestrating data and training pipelines (e.g. via Airflow) and robust feature engineering. • Excellent SQL and competence with reproducible analysis and modeling in Python. • Solid statistical foundations, including experiment design and analysis (A/B and multivariate) and inferential, causal, and predictive methods. • Familiarity with data modeling and dimensional design. • Strong command over the entire ML lifecycle, from problem formulation and data auditing through modeling, deployment, interpretation, and presentation. • Familiarity with product data (impressions, events, etc.) and product health measurement (conversion, engagement, retention, etc.). • Experience with LLMs and NLP-based solutions for data enrichment and smart automation is a plus. • Familiarity with BigQuery and the Google Cloud Platform is a plus. Qualifications • Bachelor's degree in engineering, computer science, technology, or similar fields. A postgraduate degree is a plus but not required. • 5+ years of experience across data science, machine learning engineering, and generative AI, including shipping ML models to production. • Experience building ML systems in an online consumer product setting is a plus. • A good problem solver with a 'figure it out' growth mindset. • An excellent collaborator. • An excellent communicator. • A strong sense of ownership and accountability. • A 'keep it simple' approach to #makeithappen. Who we are Since launching in Kuwait in 2004, talabat, the leading on-demand food and Q-commerce app for everyday deliveries, has been offering convenience and reliability to its customers. talabat’s local roots run deep, offering a real understanding of the needs of the communities we serve in eight countries across the region. We harness innovative technology and knowledge to simplify everyday life for our customers, optimize operations for our restaurants and local shops, and provide our riders with reliable earning opportunities daily. Here at talabat, we are building a high performance culture through engaged workforce and growing talent density. We're all about keeping it real and making a difference. Our 6,000+ strong talabaty are on an awesome mission to spread positive vibes. We are proud to be a multi great place to work award winner.

Company Description: Since launching in Kuwait in 2004, talabat, the leading on-demand food and Q-commerce app for everyday deliveries, has been offering convenience and reliability to its customers. talabat’s local roots run deep, offering a real understanding of the needs of the communities we serve in eight countries across the region. We harness innovative technology and knowledge to simplify everyday life for our customers, optimize operations for our restaurants and local shops, and provide our riders with reliable earning opportunities daily. Here at talabat, we are building a high performance culture through engaged workforce and growing talent density. We're all about keeping it real and making a difference. Our 6,000+ strong talabaty are on an awesome mission to spread positive vibes. We are proud to be a multi great place to work award winner. Job Description: As the leading delivery company in the region, we have a great responsibility and opportunity to impact the lives of millions of customers, restaurant partners, and riders. To realize our potential, we need to advance our platform to become much more intelligent in how it understands and serves our users. As a Staff AI Scientist on the global AI hub, your mission will be to improve the quality of the decisions made across product and business via relevant, reliable, and actionable data. You will own a particular domain across product and business and will work closely with the corresponding product and business managers as part of a talented team of data scientists and data engineers. You will own the entire data value chain, including logging, data modeling, analysis, reporting, and experimentation. Many of our initiatives will focus on leveraging Generative AI for tasks such as data enrichment, smart content understanding, and automated decision-making to enhance user experiences and business operations. Responsibilities • Leveraging ambiguous business problems as opportunities to drive objective criteria using data. • Solving complex business problems using the simplest, most appropriate algorithms to deliver business value. • Designing and implementing effective and impactful machine learning and generative AI systems in production. • Developing a deep understanding of the product experiences and business processes that make up your area of focus. • Developing a deep familiarity with the source data and its generating systems through documentation, interacting with the engineering teams, and systematic data profiling. • Contributing heavily to the design and maintenance of the data models that allow us to measure performance and comprehend performance drivers for your area of focus. • Working closely with product and business teams to identify important questions that can be answered effectively with data. • Delivering well-formed, relevant, reliable, and actionable insights and recommendations to support data-driven decision-making through deep analysis and automated reports. • Designing, planning, and analyzing experiments (A/B and multivariate tests). • Supporting product and business managers with KPI design and goal setting. • Mentoring other data scientists in their growth journeys. • Contributing to improving our ways of working, our tooling, and our internal training programs. Qualifications: Requirements Technical Experience • Experience in machine learning, generative AI, deep learning, recommendation systems, pattern recognition, data mining, and artificial intelligence. • Deep knowledge and experience in ML and GenAI frameworks (e.g. Scikit-learn, XGBoost, LightGBM, CatBoost, SVMs, Keras, TensorFlow, PyTorch, Hugging Face Transformers, LLM fine-tuning). • Excellent SQL. • Competence with reproducible data analysis using Python or R. • Familiarity with data modeling and dimensional design. • Strong command over the entire data lifecycle, including problem formulation, data auditing, rigorous analysis, interpretation, recommendations, and presentation. • Familiarity with different types of analysis, including descriptive, exploratory, inferential, causal, and predictive analysis. • Deep understanding of various experiment design and analysis workflows and the corresponding statistical techniques. • Familiarity with product data (impressions, events, etc.) and product health measurement (conversion, engagement, retention, etc.). • Experience with LLMs and NLP-based solutions for data enrichment and smart automation is a plus. • Familiarity with BigQuery and the Google Cloud Platform is a plus. • Data engineering and data pipeline development experience (e.g. via Airflow) is a plus. Qualifications • Bachelor's degree in engineering, computer science, technology, or similar fields. A postgraduate degree is a plus but not required. • 7+ years of experience working in data science, machine learning, and Gen AI. • Experience doing data science in an online consumer product setting is a plus. • A good problem solver with a ‘figure it out’ growth mindset. • An excellent collaborator. • An excellent communicator. • A strong sense of ownership and accountability. • A ‘keep it simple’ approach to #makeithappen. Additional Information: