Data Scientist - Time Series Forecasting

Role Overview

Our client, a technology-focused firm based in Dublin, Ireland, is looking to appoint a skilled Data Scientist specializing in time-series forecasting to join their expanding team. This position entails developing, refining, and deploying forecasting models to extract actionable insights and support business decisions.

Core Responsibilities

• Analyze extensive and complex datasets to detect meaningful trends and commercial insights.
• Design, test, and optimize machine learning and statistical models, with a focus on time-series forecasting techniques.
• Choose suitable modeling strategies and assess their performance using appropriate validation metrics.
• Perform feature engineering and thorough data preparation to enhance model effectiveness.
• Translate complex analytical results into clear, practical recommendations for stakeholders of varying technical backgrounds.
• Collaborate with engineering teams to deploy and integrate models within production pipelines.
• Maintain, monitor, and update models for sustained performance and reliability.
• Partner with internal teams to ensure solutions meet both business goals and customer needs.
• Contribute to evolving data science methodologies, tools, and standards to support team growth.
• Keep abreast of latest advancements in forecasting, machine learning, and applied data science fields.

Experience & Qualifications

• At least 3 years' experience working in roles such as Data Scientist, Applied Scientist, Machine Learning Scientist, or similar data analytical positions.
• Hands-on experience in constructing machine learning/statistical time-series forecasting models.
• In-depth understanding of statistical theory, ML algorithms, experimental design, and evaluation frameworks.
• Strong proficiency in Python and data science libraries like pandas, NumPy, and scikit-learn.
• Proven ability in processing, exploring, and preparing structured data for analysis.
• Expertise in exploratory data analysis and crafting impactful features.
• Competence in interpreting complex models and communicating findings effectively to both technical and non-expert audiences.
• Experience managing full-cycle data science projects from definition to deployment.
• Capability to work autonomously while contributing constructively to collaborative efforts.

Preferred Additional Skills

• Familiarity with deploying and monitoring data science models in production settings.
• Knowledge of MLOps frameworks and automated pipeline development.
• Experience with cloud computing platforms such as AWS, Azure, or Google Cloud.
• Working knowledge of SQL and data visualization tools.
• Proficiency with forecasting techniques and libraries, including ARIMA, Prophet, gradient boosting, or deep-learning forecasting methods.
• Understanding of data engineering and scalable pipeline construction.
• Experience with computer vision or other machine learning applications is a bonus.
• Background in client-facing analytical or technical roles.
• Aspirations toward advancing into technical or data science leadership roles.

Educational Background

• Bachelor’s degree in Data Science, Statistics, Mathematics, Computer Science, AI, Engineering, Economics, or a related quantitative discipline.
• Postgraduate credentials in relevant fields are advantageous but not mandatory.
• Equivalent professional experience may substitute formal education requirements.

Working Conditions & Benefits

• Permanent full-time employment.
• Standard business hours, Monday to Friday.
• Flexible or hybrid working options depending on company needs.
• Competitive remuneration and benefits package.
• Professional growth opportunities including paths to technical or leadership roles.
• Engagement with cutting-edge data science projects and technologies.
• Ability to influence and develop data science best practices within an expanding team.