Role Description We are looking for an experienced and driven Head of Data Science to lead our data science and machine learning efforts across our three international markets: the Philippines, Vietnam, and Thailand. This is a senior leadership role that sits at the intersection of advanced analytics, credit risk, and regional business strategy. - Lead and mentor a centralized team of data scientists based in Singapore, serving the Philippines, Vietnam, and Thailand markets. - Define the data science roadmap aligned with business priorities in each market. - Build a high-performance, collaborative team culture and establish best practices in model development, validation, and deployment. - Manage hiring, onboarding, and career development of data science talent. - Own the end-to-end design, development, validation, and deployment of credit risk models (e.g., Application and Behaviour risk scorecards, transaction models). - Remain hands-on and lead by example, actively contributing to data analysis, model building, and technical problem solving alongside the team. - Expand the team's capabilities beyond credit risk to include other ML use cases such as fraud detection, customer segmentation, churn prediction, and propensity modeling. - Collaborate with ML engineering teams to integrate models into operational systems and data pipelines. - Act as the primary DS technical point of contact for business stakeholders in the Philippines, Vietnam, and Thailand. - Translate complex model outputs and data science insights into clear, actionable recommendations for non-technical audiences. - Regularly present model performance updates, risk insights, and strategic recommendations to senior leadership and country heads. - Work closely with local risk, product, and operations teams to ensure models address real business challenges. - Champion data-driven decision making across all three markets. Qualifications - Minimum 10 years of experience in data science or machine learning, with at least 3–5 years in a team leadership or management role. - Deep domain expertise in credit risk modeling and/or the financial services industry (lending, banking, fintech, or similar). - Strong technical skills across the full data science lifecycle, from data analysis and feature engineering to model development and production deployment. - Excellent communication and stakeholder management skills, with the ability to navigate cross-cultural business environments. - Experience in Southeast Asian financial markets, particularly the Philippines, Vietnam, or Thailand, is a plus. Benefits - Lead a high-impact data science function across three fast-growing markets in Southeast Asia. - Shape the ML strategy from the ground up, you will have real ownership and influence over how models are built and applied, and play a key role in building and shaping the data science team itself. - Work with a passionate, cross-functional team that values data-driven thinking and experimentation.

• Lead the design, development, and governance of enterprise data pipelines and metadata frameworks within Palantir Foundry and integrated data platforms. • Serve as the technical and functional lead for data ingestion, transformation, and metadata management across structured and unstructured data sources. • Define and enforce metadata standards, data models, ontologies, and data dictionaries to enable scalable search, analytics, and cross-system integration. • Oversee implementation of end-to-end data pipelines, including ingestion, validation, transformation, and delivery into downstream platforms (e.g., Palantir, Databricks). • Establish and govern data quality, validation, and exception handling processes, ensuring completeness, accuracy, and traceability of data assets. • Ensure alignment of pipelines and metadata with enterprise architecture, system-of-record requirements, and integration patterns. • Ensure pipelines effectively support document-based ingestion workflows, including integration with OCR/ICR outputs and downstream metadata extraction processes. • Enable AI-ready data foundations, supporting downstream capabilities such as semantic search, entity resolution, and advanced analytics. • Partner with data science and engineering teams to ensure data pipelines and metadata support AI/ML use cases and analytical workflows. • Drive data governance and lifecycle management, including schema versioning, lineage tracking, auditability, and compliance with security and privacy requirements. • Oversee integration across platforms (e.g., AWS, Databricks, Palantir), ensuring scalable, secure, and reliable data exchange. • Lead and mentor teams in a matrixed, cross-functional environment, providing technical direction and quality oversight. • Engage with senior stakeholders to define data strategy, prioritize initiatives, and translate business needs into technical solutions. • Operate within an Agile delivery model, overseeing backlog prioritization, technical design reviews, and iterative delivery across workstreams.

Hello, let’s meet! Who We Are While Xebia is a global tech company, our journey in CEE started with two Polish companies – PGS Software, known for world-class cloud and software solutions, and GetInData, a pioneer in Big Data. Today, we’re a team of 1,000+ experts delivering top-notch work across cloud, data, and software. And we’re just getting started. What We Do We work on projects that matter – and that make a difference. From fintech and e-commerce to aviation, logistics, media, and fashion, we help our clients build scalable platforms, data and AI solutions, and cutting-edge applications to shape the future of tech. Our clients include McLaren, Aviva, Deloitte, Spotify, Disney, ING, UPS, Tesco, Truecaller, AllSaints, Volotea, Schmitz Cargobull, Allegro, InPost, and many, many more. We value smart tech, real ownership, and continuous growth. We use modern, open-source stacks, and we’re proud to be trusted partners of Databricks, dbt, Snowflake, Azure, GCP, and AWS. Fun fact: we were the first AWS Premier Partner in Poland! Beyond Projects What makes Xebia special? Our community. We support tech communities, organize meetups (Software Talks, Data Tech Talks), and have a culture that actively support your growth via Guilds, Labs, and personal development budgets — for both tech and soft skills. It’s not just a job. It’s a place to grow. What sets us apart? Our mindset. Our vibe. Our people. And while that’s hard to capture in text – come visit us and see for yourself. You will be: - designing and implementing data ingestion, transformation and storage layers within an Azure lakehouse architecture, - building and maintaining data pipelines using Microsoft Fabric (Lakehouse, Data Engineering, Pipelines), - translating business and analytical requirements into scalable data models and pipelines, - implementing data quality, governance and security standards (GDPR, ISO‑aligned practices), - supporting analytical and reporting use cases, including Power BI consumption, - collaborating closely with Data Architects, Business Analysts and Delivery Manager in a project‑based engagement, - contributing to platform evolution decisions (architecture, cost model, scalability, data sharing), - ensuring production‑ready, maintainable and well‑documented data solutions. Your profile: - strong experience as a Data Engineer in cloud environments, - solid knowledge of Azure data services, - experience with lakehouse architecture and modern data platforms, - proficiency in SQL and Python, - experience building production‑grade data pipelines and data models, - good understanding of data governance, security and GDPR requirements, - ability to work independently and take ownership of delivered components, - strong communication skills and experience working with business and technical stakeholders, - hands‑on experience with Microsoft Fabric or Databricks, - practical experience using AI-powered assistants (e.g. Claude Code, GitHub Copilot, Cursor) to improve productivity, quality, or decision-making in software delivery. Work from the European Union region and a work permit are required. Nice to have: - familiarity with enterprise data governance frameworks, - experience preparing data platforms for AI / ML use cases, - knowledge of data sharing patterns and external data consumers, - experience applying GenAI in a more structured way within the SDLC, including defined workflows, prompt patterns, or tool integrations embedded into daily work, - interest in and familiarity with emerging AI-driven practices (e.g. agent-based workflows, automation patterns, AI-augmented development), with a willingness to explore and experiment beyond standard approaches. Recruitment Process: CV review – HR call – Interview – Client Interview – Decision

Hello, let’s meet! Who We Are While Xebia is a global tech company, our journey in CEE started with two Polish companies – PGS Software, known for world-class cloud and software solutions, and GetInData, a pioneer in Big Data. Today, we’re a team of 1,000+ experts delivering top-notch work across cloud, data, and software. And we’re just getting started. What We Do We work on projects that matter – and that make a difference. From fintech and e-commerce to aviation, logistics, media, and fashion, we help our clients build scalable platforms, data and AI solutions, and cutting-edge applications to shape the future of tech. Our clients include McLaren, Aviva, Deloitte, Spotify, Disney, ING, UPS, Tesco, Truecaller, AllSaints, Volotea, Schmitz Cargobull, Allegro, InPost, and many, many more. We value smart tech, real ownership, and continuous growth. We use modern, open-source stacks, and we’re proud to be trusted partners of Databricks, dbt, Snowflake, Azure, GCP, and AWS. Fun fact: we were the first AWS Premier Partner in Poland! Beyond Projects What makes Xebia special? Our community. We support tech communities, organize meetups (Software Talks, Data Tech Talks), and have a culture that actively support your growth via Guilds, Labs, and personal development budgets — for both tech and soft skills. It’s not just a job. It’s a place to grow. What sets us apart? Our mindset. Our vibe. Our people. And while that’s hard to capture in text – come visit us and see for yourself. You will be: - designing, deploying, and maintaining applications running on Kubernetes, - integrating open‑source components into a unified analytics platform, - developing internal services and tools in Python (e.g. FastAPI, Streamlit), - managing deployments using Helm, Docker, Git (GitLab) and ArgoCD, - supporting and evolving tools used by analysts and data scientists (Airflow, JupyterHub, Superset, MLflow, Trino), - ensuring platform stability, scalability, and security, - collaborating closely with data, analytics, and infrastructure teams, - participating in architectural decisions and technology choices. Your profile: - strong experience with Python, used for building data-related applications and services, - background in data engineering or working with data/analytics platforms, - very good knowledge of Kubernetes, including deployment, operations, and troubleshooting, - hands-on experience with Docker and Helm, - experience working with Git-based workflows (e.g. GitLab), - solid understanding of Linux-based environments, - ability to work across development and infrastructure domains, - experience with big data technologies such as Spark or HDFS, - familiarity with SQL engines (e.g. Trino), - experience operating open-source data tools in production environments, - understanding of CI/CD practices in platform or engineering teams, - good communication skills and strong sense of ownership, - good command of Polish (C1) and English (min. B2), - practical experience using AI-powered assistants (e.g. Claude Code, GitHub Copilot, Cursor) to improve productivity, quality, or decision-making in software delivery. - Work from the European Union region and a work permit are required. Nice to have: - familiarity with tools such as Airflow, MLflow, or Superset, - experience with platform and DevOps tools (e.g. ArgoCD, HashiCorp Vault), - knowledge of data governance or security tools (e.g. Apache Ranger, metastore solutions), - experience with frameworks such as FastAPI or Streamlit, - previous experience transitioning from a data engineering role into a platform-focused role, - experience applying GenAI in a more structured way within the SDLC, including defined workflows, prompt patterns, or tool integrations embedded into daily work, - interest in and familiarity with emerging AI-driven practices (e.g. agent-based workflows, automation patterns, AI-augmented development), with a willingness to explore and experiment beyond standard approaches. Recruitment Process: CV review – HR call – Interview – Client Interview – Decision