Data Science; Talent Network

As Imrandd continues to grow and evolve, we're investing in the future of our Data Science capability by building a strong talent pool of skilled professionals. With increasing demand from existing clients, exciting new projects and opportunities through upcoming tenders, we're always looking to connect with talented individuals who can help us deliver innovative, data-driven solutions. We're particularly interested in people who can bring fresh perspectives, new ideas, and diverse experience to complement our existing team across the following role disciplines:

• Data Analyst / Junior Data Scientists
• Data Engineers
• Data Scientists - Visualisation & Analytics
• Machine Learning Engineers

We're keen to hear from individuals at all stages of their data careers, from aspiring Data Analysts and Junior Data Scientists looking to develop their skills, through to experienced Data Engineers, Data Scientists and Machine Learning Engineers. We value people who are curious, collaborative and passionate about using data to solve problems, whether that's transforming messy industrial datasets into actionable insights, building robust cloud-based data platforms, creating compelling visualisations for decision-makers, or developing innovative AI and machine learning solutions.

Experience in engineering, energy, infrastructure or other data-rich environments is particularly welcome, but above all we're looking for people who bring fresh thinking, technical excellence and a desire to continuously learn and innovate alongside our existing team.

Experience:

Entry Level / Graduate

Focused on turning challenging, real-world industrial data into clean, analysis-ready datasets. You'll spend much of your time wrangling inspection records, anomaly registers and engineering exports, and producing early-stage analysis that feeds into larger client deliverables. It's a hands-on learning role with a clear pathway for progression into a Data Scientist role.

Key responsibilities

• Clean, validate and structure raw datasets from a wide variety of sources (Excel, CSV, PDF exports, database extracts).
• Perform exploratory data analysis to surface patterns, outliers and data-quality issues.
• Produce first-pass summaries, tables and simple visualisations to support senior team members.
• Write reusable, well-documented Python for repeatable data-cleaning tasks.
• Support data validation and QA on deliverables before they reach clients.

Essential skills & experience

• Degree in a quantitative or technical discipline (data science, engineering, maths, physics, computing or similar).
• Working knowledge of Python with pandas and numpy.
• Comfortable with spreadsheets and basic SQL.
• Strong attention to detail and a methodical approach to messy data.
• Clear written communication and willingness to learn.

Desirable

• Exposure to version control (Git).
• Experience with engineering, energy or other heavily regulated industrial data.
• Basic familiarity with a visualisation tool (Power BI, matplotlib, plotly).

Experience / Level: Mid-Level

This role owns the data infrastructure that everything else sits on. You'll design and maintain cloud-based pipelines and storage that ingest large, varied datasets - including document corpora and engineering data - and make them queryable and reliable for analysts, dashboards and ML workloads.

Key Responsibilities

• Design, build and maintain ETL/ELT pipelines on AWS.
• Manage relational, No
  
  SQL and vector databases, and choose the right store for the job.
• Build and operate data services in containers, with appropriate orchestration and scheduling.
• Implement monitoring, logging and CI/CD so pipelines are observable and reproducible.
• Work with the data science and ML teams to provision data and embeddings for downstream use.

Essential Skills & Experience

• Strong Python and SQL.
• Hands-on AWS (e.g. S3, RDS, App Runner / ECS, Secrets Manager, IAM).
• Relational databases (PostgreSQL) and at least one No
  
  SQL store (e.g. MongoDB).
• Containerisation with Docker / Docker Compose.
• Experience with task queues / orchestration (Celery, Airflow or similar).

Desirable

• Vector databases (Milvus, or alternatives such as Pinecone / pgvector) and an understanding of embedding-based retrieval.
• CI/CD pipelines and infrastructure-as-code.
• Experience handling unstructured data (documents, drawings, scanned records) at scale.

Experience:

Mid Level

The bridge between data and decision-makers. You'll turn complex datasets into clear, client-facing dashboards and analysis that drive operational and integrity decisions. This role blends solid statistical analysis with strong storytelling through visualisation.

Key Responsibilities

• Design and build interactive dashboards and reports for internal and client use.
• Perform statistical analysis (trends, correlations, anomaly/outlier detection) and translate findings into clear recommendations.
• Work directly with stakeholders to understand requirements and iterate on deliverables.
• Build…