Data Scientist

• Defines, owns, and implements the Enterprise Data Governance Strategy, ensuring alignment with the organization’s overall data and digital strategy.
• Establishes and oversees data governance policies, standards, and operating models, including frameworks for data quality, metadata, and master data management.
• Ensures the effective design and governance of data architecture and data zones within the organization’s data platforms, driving standardization and reusability.
• Oversees the creation and maintenance of enterprise data models (conceptual, logical, and physical), ensuring consistency and alignment with business needs.
• Drives data literacy and accountability across all business functions by establishing data governance KPIs, metrics, and reporting.
• Engages with senior business stakeholders, regulatory bodies, and external partners to ensure compliance with internal and external data management standards.
• Provides thought leadership on emerging data governance trends, tools, and technologies, guiding continuous improvement initiatives.
• Oversees the profiling, onboarding, and certification of new data sources, ensuring adherence to governance and quality standards.
• Acts as the Product Owner for data-related transformation inititiaves that involve ingesting new sources of data or improving the quality of existing data.

Head of DNA Data Governance

• The Senior Data Scientist is responsible for the development and deployment of predictive models, transforming complex data into scalable, high-impact analytical solutions.
• Minimum 4 years of hands-on experience in Data Science, ΑΙ, Machine Learning or Advanced Analytics.
• Proven experience designing and implementing end-to-end data science solutions for complex business problems.
• Demonstrated experience taking solutions from proof of concept to production, including testing, packaging, deployment, monitoring and continuous improvement.
• Strong hands-on programming experience in Python, including object-oriented programming, modular code design, exception handling, logging, unit testing and development of reusable, maintainable and production-ready solutions.
• Hands-on experience with Databricks, Apache Spark and PySpark for large-scale data processing, analytics and development of scalable solutions.
• Experience with Git-based development, version control, code reviews, CI/CD practices, model deployment, monitoring and lifecycle management.
• Advanced knowledge of SQL and experience working with large and complex datasets, including data extraction, transformation, validation and query optimization.
• Familiarity with AWS cloud environments and services supporting data storage, processing and application integration will be considered an advantage.
• Strong knowledge of machine learning and data science libraries, including pandas, NumPy, scikit-learn and MLflow.

Senior Data Scientist

• **What Your Day Might Look Like:**
• - Specify Bayesian regression models from scratch — likelihood, priors, hierarchical structure — not just library defaults
• - Build and diagnose models in PyMC and/or Stan — MCMC sampling, convergence diagnostics (R-hat, ESS, divergences), and posterior visualisation with ArviZ
• - Design hierarchical structures with partial pooling and multi-level architectures, handling sparse group data gracefully
• - Chain models correctly — sequential, multi-stage architectures with full uncertainty propagation (Monte Carlo through the chain, never point estimates)
• - Model media dynamics — adstock and saturation transformations (geometric, Weibull, Hill), with parameters specified via priors and response curves extracted from posteriors
• - Optimise budgets under constraints — scipy.optimize and CVXPY for channel floors/ceilings and portfolio constraints, plus multi-objective work (Pareto frontiers, weighted utility, conflicting objectives)
• - Establish causal ground truth — geo experiment design and analysis, Difference-in-Differences, Synthetic Control, and experiment-to-model calibration
• - Handle structural breaks — piecewise regression, change-point detection (PELT, BOCPD, Bayesian), and regression discontinuity design
• - Document your work — methodology docs, assumption logs, and structured write-ups that stand up to scrutiny

Data Scientist – MMO Enhancements

• Design and build customer segmentation solutions on payment transaction data, from feature engineering through clustering to business-ready segment narratives
• Engineer behavioural features at scale — multi-window RFM, spend trajectory, recency decay, and time spine construction on transactional datasets
• Validate and stress-test clusters using silhouette scores, stability testing, and — most importantly — business interpretability
• Explain your models using SHAP, translating feature importance into plain-English segment stories for non-technical stakeholders
• Calculate and interpret competitive metrics such as Spend Index (issuer view) and Wallet Share (merchant view), including a correct treatment of network coverage limitations
• Build media planning logic — temporal disaggregation of coarse budgets into monthly plans (Denton-Cholette or equivalent), heuristic channel splits, and MMO response curve integration
• Handle data filtering with rigour — BIN/ICA logic for issuer data, merchant_parent_name logic for merchant data
• Document your work — methodology docs, assumption logs, and structured write-ups that others can pick up and run with

Data Scientist – Intelligent Media Planning

• Develop and own credit risk models, including scorecards, decision trees, and machine learning classifiers (e.g. PD, LGD, EAD estimation aligned with CRR/CRD expectations where applicable);
• Build and maintain time-series models for portfolio monitoring, early warning signals, and stress testing (EBA Guidelines on stress testing & IRB where relevant);
• Translate models into production-ready decisioning logic (rules, cut-offs, segmentation) within loan origination and servicing systems;
• Design and refine underwriting policies, ensuring alignment with EBA Guidelines on Loan Origination & Monitoring and consumer credit regulations;
• Conduct portfolio analytics, backtesting, and model performance monitoring (discrimination, calibration, stability, fairness considerations);
• Support regulatory compliance (model governance, documentation, audit trails, explainability, GDPR considerations in automated decision-making);
• Collaborate with product, engineering, and data teams to embed risk logic into customer journeys across online and merchant channels;
• Operate in a hands-on capacity, directly executing analyses, model builds, and implementations in a lean team environment;
• Ensure that all activities and duties are carried out in full compliance with regulatory requirements and supporting the continued implementation of the Group Anti-Bribery and Corruption Policy.

Risk Analytics & Modeling Lead

• Participate in full software development lifecycle implementing, testing and maintaining custom data solutions
• Design, develop, and deploy machine learning models, with a strong emphasis on LLMs for diverse NLP applications.
• Develop AI-powered applications and internal tools/dashboards using Python
• Build and maintain data and document processing pipelines (ingestion, parsing, chunking, embedding, retrieval)
• Integrate LLMs/VLMs into production services, including RAG-based systems
• Deploy, monitor, and scale solutions on AWS
• Collaborate with business stakeholders and the digital team to translate requirements into working systems
• Act as a technical consultant for the client
• Work as a key member of an Agile Team

Mid-Level ML Engineer, Data Scientist

• Provide project management leadership and expertise in Clinical Data Sciences
• Oversee the development cycle of database build
• Ensure compliance and operational excellence in collaboration with partners
• Solves moderately complex problems related to database build and implementation
• Identifies existing process/product improvements

Data Scientist jobs in Greece

See all jobs on JobTailor