Data Scientist

Fruition Group Ireland; Dublin; Hybrid; EUR 75,000 - 110,000; Full time

Job summary

Fruition Group Ireland is seeking an experienced Data Scientist to develop forecasting solutions in a technology-focused environment. You will analyze large datasets, build time-series models and translate results into actionable business insights.

Responsibilities include model development, validation, deployment and collaboration with customers and internal teams. Hybrid working in Ireland is supported, with opportunities for leadership as you grow.

Qualifications

• 3+ years in data science or related analytical role.
• Experience building models for time-series forecasting.
• Strong stats, ML fundamentals and model evaluation knowledge.

Responsibilities

• Explore large datasets to identify trends and insights.
• Develop and optimise time-series models for forecasting.
• Select modelling approaches and validate performance.
• Perform feature engineering and data preprocessing.
• Communicate results to technical and non-technical stakeholders.
• Collaborate with engineers to deploy models in production.
• Monitor models and support ongoing improvements.
• Coordinate with teams to align data science with business needs.
• Contribute to data science standards and tooling.
• Stay updated on forecasting and ML advancements.

Skills

Python

Pandas

NumPy

scikit-learn

Time-series forecasting

MLOps

AWS

Azure

GCP

SQL

Data visualization

Education

Data Science degree

Postgrad beneficial

Quantitative field

Tools

SQL

Git

AWS

Job description

Data Scientist - Time-Series Forecasting

Job Description

Our client is seeking an experienced Data Scientist to develop and enhance data-driven forecasting solutions within a growing technology environment.

This role will suit someone with a strong background in statistical modelling, machine learning and time-series analysis who enjoys transforming complex datasets into practical insights. You will work closely with technical colleagues, customers and business stakeholders to deliver forecasting solutions that support real-world decision-making.

Job Responsibilities

• Explore and analyse large, complex datasets to identify trends, patterns and commercially valuable insights.
• Develop, test and optimise statistical and machine learning models, with a particular focus on time-series forecasting.
• Select appropriate modelling approaches and evaluate performance using relevant validation techniques and metrics.
• Carry out feature engineering, data preparation and exploratory data analysis to improve model outcomes.
• Communicate findings and translate technical results into clear, actionable recommendations for customers and non-technical stakeholders.
• Work with engineering colleagues to integrate and deploy data science models into production environments.
• Support model monitoring, retraining and ongoing performance improvement.
• Collaborate with internal teams to ensure data science solutions align with business and customer requirements.
• Contribute to the development of data science standards, tools and best practices as the team grows.
• Stay informed of developments in forecasting, machine learning and applied data science.

Experience Required

• A minimum of 3 years' experience in a Data Scientist, Applied Scientist, Machine Learning Scientist or closely related analytical role.
• Practical experience developing machine learning or statistical models for time-series forecasting.
• Strong knowledge of statistics, machine learning principles, experimental methods and model evaluation techniques.
• Proficiency in Python and commonly used data science libraries such as pandas, NumPy, scikit-learn or similar.
• Experience preparing, processing and analysing structured datasets.
• Strong exploratory data analysis and feature engineering capabilities.
• Ability to interpret model outputs and communicate findings clearly to technical and non-technical audiences.
• Experience contributing to end-to-end data science projects, from problem definition and data exploration through to model evaluation and implementation.
• Ability to work independently while contributing effectively within a collaborative technical team.
• Experience supporting the deployment or ongoing monitoring of data science models in production.
• Familiarity with MLOps practices and automated model pipelines.
• Experience with cloud platforms such as AWS, Azure or Google Cloud Platform.
• Knowledge of SQL and data visualisation tools.
• Familiarity with forecasting libraries or techniques such as ARIMA, Prophet, gradient boosting or deep-learning-based forecasting.
• Understanding of data engineering processes and scalable data pipelines.
• Experience working with computer vision or other applied machine learning models.
• Previous experience in a customer-facing technical or analytical position.
• An interest in progressing towards technical leadership, data science leadership or management.

Educational Requirements

• A third-level degree in Data Science, Statistics, Mathematics, Computer Science, Artificial Intelligence, Engineering, Economics or another quantitative discipline.
• A postgraduate qualification in a relevant subject would be beneficial but is not essential.
• Relevant professional experience may also be considered in place of a specific academic background.
• Standard Monday-to-Friday working hours.
• Flexible or hybrid working arrangements, subject to business requirements.
• Competitive salary and benefits package.
• Opportunities for professional development and progression into technical leadership.
• Exposure to modern data science technologies and high-impact customer projects.
• The opportunity to help shape data science practices within a growing team.