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.
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.
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