Data Scientist

ITSearch · Recruitment agency
Full-time•Data engineering•Ireland · Remote within Ireland · Hybrid
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At a glance

Employment type
Full-time
Workplace
Hybrid

Technologies & skills

Primary technologies

Python

Other technical skills

DatabricksPandasNumPy

What you'll be doing

Experienced Data Scientist needed to develop a prioritisation system for the utilities sector, working with sensor and alert data. Role involves end-to-end ML model development, leveraging Databricks, and collaborating with cross-functional teams. Hybrid position in Dublin, 6-month contract with possible extension.

  • Design, build, and deploy ML models in a production environment
  • Analyse and interpret large-scale sensor and alert datasets
  • Collaborate with cross-functional teams to translate business needs into data-driven solutions
  • Apply end-to-end data science methodologies including requirements gathering, feature engineering, model development, testing, deployment, and results communication
  • Leverage Databricks for scalable data and ML workflows

Key requirements

Must-have

  • 5+ years’ experience in ML/AI with a record of delivering production-ready systems
  • Strong background in Python (pandas, numpy, scikit-learn, scipy) and core ML algorithms
  • Hands-on experience implementing AI solutions in production environments
  • Databricks experience
  • Excellent communication skills
  • Strong problem-solving skills
  • Ability to work collaboratively

Experience: 5+ years

Role signals

Technical focus
machine learning, data science, production systems
Hands-on vs management
Hands-on

Full job description

Data Scientist – Machine Learning (Contract)

  • Location: Dublin (Hybrid)
  • Duration: 6 months (Strong possibility of extension)

We are looking for an experienced Data Scientist to join a leading AI software house to support the development of a prioritisation system within the utilities sector. This role offers the chance to work with sensor and alert data, applying advanced ML techniques to solve real-world operational challenges.

Key Responsibilities

  • Design, build, and deploy ML models in a production environment.

  • Analyse and interpret large-scale sensor and alert datasets to inform decision-making.

  • Collaborate with cross-functional teams to translate business needs into data-driven solutions.

  • Apply end-to-end data science methodologies: requirements gathering, feature engineering, model development, testing, deployment, and results communication.

  • Leverage Databricks to enable scalable data and ML workflows.

About You

  • 5+ years’ experience in ML/AI with a proven record of delivering production-ready systems.

  • Strong background in Python (pandas, numpy, scikit-learn, scipy) and core ML algorithms.

  • Hands-on experience implementing AI solutions in production environments.

  • Databricks experience (heavily prioritised).

  • Excellent communicator, able to present complex technical solutions to diverse stakeholders.

  • Strong problem-solving skills and ability to work collaboratively in dynamic settings.

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Interview prep pack

Grounded in this listing. Use it to prepare examples before you apply.

Your interview focus

Based on this listing, the role is for an experienced Data Scientist to design, build, and deploy machine learning models using Python and Databricks, focusing on large-scale sensor and alert data within the utilities sector. The position emphasizes end-to-end ML workflow ownership, production deployment, and strong communication with cross-functional teams.

  • Databricks hands-on expertise·High
  • Production ML deployment experience·High
  • Advanced Python data science stack·High
  • Communication with stakeholders·Medium

Only have 30 minutes?

Follow a focused preparation plan based on this job.

Start 30-minute prep

Your 30-minute plan

  1. Review and Summarize End-to-End ML Project Experience

    0–8 min

    Prepare concise stories highlighting your role in designing, deploying, and maintaining production ML systems, focusing on business impact.

  2. Refresh Databricks Knowledge

    8–15 min

    Study Databricks documentation and recall specific examples where you used it for scalable data processing or ML workflows.

  3. Research Sensor and Alert Data Use Cases

    15–20 min

    Read about common challenges and solutions in handling sensor and alert data, especially in the utilities sector.

  4. Practice Explaining Technical Concepts

    20–25 min

    Rehearse explaining complex ML concepts and project outcomes to non-technical audiences using clear language and visuals.

  5. Prepare Targeted Questions for the Interviewer

    25–30 min

    Select and tailor questions to ask about the project, team, and technical environment to demonstrate your engagement and fit.

Likely questions

, 7 items

Priority reflects how strongly this topic is emphasised in the job listing, not whether it will be asked.

Talking points

, 6 items

  • Productionizing Machine Learning Models

    Demonstrate your experience taking ML models from development to deployment, including monitoring and maintenance.

  • Working with Large-Scale Sensor and Alert Data

    Showcase your ability to handle, preprocess, and extract insights from complex, high-volume datasets relevant to the utilities sector.

  • Leveraging Databricks for Scalable ML Workflows

    Highlight your hands-on experience with Databricks, especially for distributed data processing and model training.

  • Feature Engineering and Model Evaluation

    Explain your approach to creating effective features and evaluating model performance in real-world scenarios.

  • Translating Business Needs into Data Solutions

    Provide examples of collaborating with non-technical teams to deliver actionable, data-driven results.

  • Communicating Technical Results to Stakeholders

    Demonstrate your ability to present complex findings clearly to both technical and non-technical audiences.

What to research

, 4 items

  • Review Databricks Features and Best Practices

    Refresh your knowledge of Databricks, focusing on scalable data processing, ML workflows, and integration with Python libraries.

  • Prepare Examples of End-to-End ML Projects

    Select and structure stories that demonstrate your experience designing, deploying, and maintaining production ML systems.

  • Understand Sensor and Alert Data Challenges

    Research common issues and solutions in handling large-scale sensor and alert datasets, especially in the utilities sector.

  • Practice Communicating Technical Concepts

    Prepare to explain complex ML concepts and project outcomes to both technical and non-technical stakeholders.

Questions to ask

, 6 items
  1. What are the main business objectives for the prioritisation system in the utilities sector?

    Why ask this? Clarifies the impact and expectations for your work.

  2. How is the team structured and how do data scientists collaborate with other departments?

    Why ask this? Helps you understand cross-functional collaboration and communication expectations.

  3. What are the biggest technical challenges currently faced with sensor and alert data?

    Why ask this? Identifies key problem areas where your expertise can add value.

  4. What tools and processes are in place for deploying and monitoring ML models in production?

    Why ask this? Gives insight into the maturity of the ML infrastructure and your potential responsibilities.

  5. How is success measured for data science projects in this organization?

    Why ask this? Clarifies performance metrics and expectations.

  6. Is there an opportunity to extend the contract or transition to a permanent role?

    Why ask this? Helps you plan for long-term career prospects.

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Apply NowApply before: 15 Oct 2026