Data Scientist

Supply Wisdom

Data Scientist developing and maintaining predictive models and data pipelines for risk intelligence at Supply Wisdom. Collaborating with product and engineering teams to solve complex data problems.

Posted 7/29/2026full-timeRemote • 🇮🇪 IrelandMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and maintaining data pipelines and machine learning models, with strong proficiency in Python and data science libraries. Capable of effectively communicating technical concepts to non-technical stakeholders while independently managing complex projects.

Highest-signal resume keywords

Python ProgrammingMachine Learning Model DevelopmentData Pipeline EngineeringDatabase Design and QueryingCommunication Skills

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ScienceMachine LearningPredictive ModelingClassification ModelsData PreparationData IntegrationModel DeploymentPerformance MonitoringStatistical AnalysisData Cleaning

Soft Skills

Written CommunicationVerbal CommunicationProblem SolvingIndependenceJudgment

Tools & Technologies

PandasNumPyScikit-LearnTensorFlowPyTorchDjango Rest Framework

Industry Keywords

Risk IntelligenceFinancial DomainCybersecurityOperational RiskESG Compliance

Tech Stack

Tools & technologies

DjangoNumpyPandasPythonPyTorchScikit-LearnTensorflow

About the role

Key responsibilities & impact

Take on ambiguous, open-ended problems (e.g. "improve target coverage in this risk domain" or "reduce false positive rate for this classifier") and independently structure an approach, build it, and iterate toward a working solution.
Design, build, and maintain data pipelines and ML models that identify, detect, and quantify risk intelligence across financial, cyber, operational, ESG, and compliance domains.
Prepare, clean, and structure large and often messy datasets for modeling, exercising judgment on where automation, direct data integration, or LLM-based approaches each make the most sense.
Build and continuously refine predictive and classification models (e.g. credibility scoring, urgency/severity classification, entity resolution), evaluating performance against real outcomes and iterating based on data-driven feedback.
Engage directly with product, engineering, and occasionally customer-facing stakeholders to translate business and methodology questions (e.g. model bias, data confidence, coverage limitations) into clear technical answers and solutions.
Use Python and standard data science/ML libraries alongside strong database and querying skills to move fluidly from data prep to modeling to production.
Proactively evaluate and adopt new tools and techniques, including AI-assisted workflows, to accelerate your own delivery rather than defaulting to manual or established methods.
Document your methodology, code, and data schemas clearly enough that teammates and stakeholders can build on your work.

Requirements

What you’ll need

3-5 years of experience in applied data science, machine learning, or a closely related technical role.
Strong Python skills across the data science and ML stack (pandas, NumPy, scikit-learn, TensorFlow, PyTorch or equivalent).
Solid database and data engineering fundamentals — comfortable designing schemas, writing efficient queries, and building reliable pipelines, not just consuming clean data.
Experience deploying models into production and monitoring their performance, not just building them in a notebook.
Excellent written and verbal communication skills; able to explain technical tradeoffs to non-technical stakeholders.
A track record of working with minimal supervision and following through on open items without prompting.
Degree in Computer Science, Applied Mathematics, Statistics, or a related field, or equivalent practical experience.
Nice to have: Exposure to REST API design and development (Django Rest Framework or similar).

Benefits

Comp & perks

Health insurance
Professional development opportunities