Data Scientist - Supply Chain Predictive Maintenance, Dublin

Job Summary Startup Inno is seeking a highly motivated and analytical

Data Scientist– Supply Chain Predictive Maintenance

to join our growing data and analytics team. In this role, you will be responsible for designing and deploying advanced predictive models that help optimize supply chain operations, reduce downtime, and enhance asset reliability. You will work closely with cross‑functional teams including operations, engineering, IT, and business stakeholders to transform complex data into actionable insights that drive operational excellence.

This position is ideal for a data professional who is passionate about applying machine learning and statistical modeling to real‑world industrial challenges, particularly in the areas of logistics, manufacturing, and asset management.

Key Responsibilities

Develop, implement, and maintain predictive maintenance models for supply chain and operational assets.

Analyze large-scale structured and unstructured datasets from IoT sensors, ERP systems, and logistics platforms.

Identify patterns, anomalies, and trends that impact equipment performance and supply chain efficiency.

Design data pipelines and workflows for data collection, cleaning, feature engineering, and model training.

Collaborate with operations and engineering teams to translate business problems into analytical solutions.

Build dashboards and reports to communicate insights and model performance to stakeholders.

Continuously monitor and improve model accuracy and reliability.

Support data‑driven decision‑making through scenario analysis and forecasting.

Ensure best practices in data governance, security, and model documentation.

Required Skills and Qualifications

Strong proficiency in Python or R for data analysis and machine learning.

Solid understanding of machine learning algorithms (regression, classification, clustering, time‑series).

Experience with data visualization tools such as Power BI, Tableau, or Matplotlib/Seaborn.

Hands‑on experience with SQL and relational/non‑relational databases.

Familiarity with cloud platforms (AWS, Azure, or GCP) and big data frameworks (Spark, Hadoop).

Knowledge of statistical modeling and predictive analytics techniques.

Ability to work with IoT or sensor data is a strong advantage.

Excellent communication skills with the ability to explain technical concepts to non‑technical audiences.

Experience

Bachelors or Masters degree in Data Science, Computer Science, Engineering, Statistics, or a related field.

2–5 years of professional experience in data science, analytics, or a related role.

Prior experience in supply chain, manufacturing, logistics, or industrial analytics is highly preferred.

Proven track record of building and deploying predictive models in a business environment.

Working Hours

Full‑time position (40 hours per week).

Flexible working hours with core collaboration hours.

Hybrid or remote working options depending on location and project requirements.

Knowledge, Skills, and Abilities

Strong analytical and problem‑solving mindset.

Ability to manage multiple projects and priorities in a fast‑paced environment.

Deep curiosity and willingness to continuously learn new tools and techniques.

Business acumen to understand operational challenges and translate them into data solutions.

High attention to detail and strong documentation skills.

Team‑oriented approach with the ability to work independently when required.

Benefits

Competitive salary and performance‑based incentives.

Health insurance and wellness programs.

Flexible work arrangements (remote/hybrid).

Professional development and training opportunities.

Access to cutting‑edge tools and technologies.

Inclusive and collaborative work culture.

Career growth opportunities in a rapidly scaling organization.

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