Data Scientist - Extensions

Role overview

This opportunity is for a Data Scientist focused on Extensions and is based in Ireland. The hiring process is handled by a partner company, which will review applications and manage the next steps.

In this role, you will help improve advanced AI systems built to support enterprise-scale decision-making. Your work will center on challenging structured data problems and on lifting the predictive quality of large tabular models across a variety of industries and practical use cases. The position combines research and production work, so you will be expected to turn experimental concepts into dependable, production-ready solutions that have a direct effect on enterprise customers. You will work closely with research, engineering, and applied AI teams to study model behavior and strengthen system capabilities. This is a highly technical, fast-paced, research-oriented environment with the chance to contribute to foundational AI technology that helps large organizations make faster, more accurate decisions.

Key responsibilities

• Develop and test advanced data science approaches that improve prediction quality on large-scale structured enterprise datasets and across different prediction problems.
• Build, support, and refine production-grade Python components with emphasis on accuracy, scalability, and reuse.
• Examine real-world enterprise data patterns and create methods that keep models robust when facing missing values, uneven class distributions, and changes in data distribution.
• Plan and execute controlled experiments, create solid benchmarks, and assess model gains using statistically reliable methods.
• Handle a wide mix of structured machine learning tasks, including classification, regression, ranking, and forecasting.
• Work in partnership with research and engineering teams to interpret model behavior and convert insights into product enhancements.
• Collaborate with Applied AI Engineers to test ideas on real customer data and turn validated findings into deployable capabilities.
• Help produce technical documentation, internal tools, and team practices that improve reproducibility and knowledge sharing.

Requirements

• At least 5 years of experience in data science, machine learning, or applied ML engineering.
• Strong working knowledge of Python, with practical use of pandas, NumPy, and scikit-learn.
• Deep experience with classic machine learning approaches such as XGBoost, LightGBM, CatBoost, and related gradient boosting tools.
• Strong understanding of tabular data issues in the real world, including missing data, class imbalance, high-cardinality features, and distribution shift.
• Proven ability to design experiments, build benchmarks, and draw careful conclusions from noisy or imperfect data.
• Capability to take projects independently from an early research idea through to a production-ready implementation.
• Strong analytical thinking and problem-solving skills, with a focus on measurable outcomes and empirical validation.
• Experience with structured prediction problems in fields such as finance, healthcare, supply chain, retail, or industrial settings is an advantage.
• Familiarity with tabular foundation models such as TabPFN or CARTE is a strong plus.
• Exposure to tools like DuckDB, Polars, or other modern in-process analytics engines is beneficial.
• Experience in competitive data science settings such as Kaggle or DrivenData, or contributions to ML libraries, would be helpful.
• Ability to understand, interpret, and apply machine learning research papers in practical work.

Perks and benefits

• Competitive pay package with salary and equity.
• Full health coverage for you and your dependents.
• Paid parental leave available to all parents, including adoptive and surrogate families.
• Relocation assistance for candidates joining office-based locations.
• A mission-driven, low-ego culture centered on ownership, collaboration, and impact.
• The chance to work on foundational AI systems that shape enterprise decision-making at scale.
• High autonomy in a research-heavy and technically demanding environment.

Additional information

The hiring partner uses an AI-supported matching process to review applications quickly, fairly, and objectively against the core requirements of the role. Shortlisted candidates are then shared directly with the hiring company. Final decisions and all follow-up steps, including interviews and assessments, are managed internally by the hiring company.

By applying, you acknowledge that the hiring partner will process your personal data for candidate evaluation and may share relevant information with the employer. This is done on the basis of legitimate interest and pre-contractual measures under applicable data protection laws, including GDPR. You may exercise your rights to access, correct, delete, or object to processing at any time.

Artificial intelligence tools may also be used to support parts of the hiring workflow, such as resume review, application analysis, and signal detection for inconsistencies or verification checks. These tools assist the recruitment team and do not replace human judgment. Final hiring decisions are made by people. For more information about data handling, you may contact the hiring team.