Data Scientist

Role: Data Science Consultant

Location: Dublin, Ireland (3days Onsite)

Permanent/Fixed Term Full Time Employment

Responsibilities

A) Data Science & AI Delivery (≈80%)

The Consultant will:

• Translate complex business problems into scientific research problems, defining hypotheses, success metrics, and experimental designs.
• Develop data‑driven solutions using appropriate techniques (AI / ML / DL / GenAI / Agentic), aligned to fraud, risk‑scoring, and security analytics use cases.
• Perform hands‑on data extraction, analysis, cleaning, preparation, modeling, and evaluation, including EDA, feature engineering, offline model development, and model performance assessment.
• Conduct data analysis to support business cases, hypothesis validation, new ideas, and proof‑of‑concepts, including building prototypes and evaluating tools or frameworks.
• Develop classification and risk‑scoring models, including:
• Calibration and mapping of model outputs to discrete score ranges
• Deterministic handling of missing or partial features
• Evaluation of confidence–coverage trade‑offs in ambiguous or weakly labeled domains.
• Lead ground‑truth and labeling strategies in weakly labeled domains, including proxy or synthetic labels, with explicit documentation of uncertainty and modeling limitations.
• Implement reason‑code and explainability frameworks that translate internal model signals into stable, human‑readable drivers, with versioned taxonomy governance that preserves explainability contracts.
• Deploy, document, maintain, and monitor developed solutions, including operational metrics, performance and drift monitoring, and incident triage support as required.
• Present results to a variety of audiences (technical and non‑technical), clearly explaining model behavior, limitations, and operational considerations.
• Where applicable and approved by Customer, contribute to protecting innovations through patents and/or academic or industry publications, and support knowledge dissemination via presentations.
• Collaborate closely with engineering, product, security, and risk teams to ensure solutions are production‑ready and usable.

B) Data Engineering Enablement (≈20%)

The Consultant will:

• Support Snowflake‑based data platforms that enable analytics and modeling workloads, including environment readiness (Dev / Stage / MTF / Prod).
• Assist with RBAC and provisioning (users, roles, warehouses) aligned with Customer standards and operating models.
• Support data ingestion and transformation pipelines that feed model features, including on‑premises to cloud ingestion and cloud storage to curated/model layers.
• Implement or enhance data quality checks, automated tests, and support CI/CD enablement (GitHub, Jenkins) for analytics and data assets.
• Support operational monitoring, including resource usage, thresholds, alerts, and Splunk integration where applicable, to ensure platform reliability for data science workloads.

All About You

Required Skills & Experience

• Proven experience delivering Machine Learning and Deep Learning solutions end‑to‑end, from data exploration through deployment and monitoring.
• Strong applied knowledge of ML, DL, GenAI, and Agentic approaches, including understanding of underlying mathematical foundations.
• Strong experience with EDA, feature engineering, offline modeling, evaluation, and transforming messy data into clean, reusable features.
• Proven ability to communicate scientifically and present results to diverse technical and non‑technical stakeholders.
• Demonstrated ability to handle data responsibly, including data governance, privacy, and standards compliance.
• Experience building GenAI‑based solutions using LLMs/SLMs, RAG, Vector databases, and agentic frameworks.
• Strong hands‑on programming experience (e.g., Python) and strong SQL skills, with the ability to work across big‑data and cloud ecosystems.
• Curious, critical thinker with strong problem‑solving skills and a hands‑on execution mindset, comfortable working in ambiguous problem spaces.

Optional / Nice to Have

• Experience in fraud, risk, or security analytics domains.
• Experience with model governance, explainability, and audit requirements in regulated environments.
• Familiarity with Snowflake and cloud‑based data platforms supporting large‑scale analytics.