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Job Summary

This is an exciting opportunity for an ambitious Data Scientist to fast-track their career development as a Knowledge Transfer Partnership (KTP) Associate, utilising skills in Data Science with a specific focus on Exploratory Data Analysis (DEA), Machine Learning, Deep Learning, and relevant skills.

You will undertake a 24-month collaborative project between Aquaterra https://aquaterra.co.uk/ and Robert Gordon University (School of Computing), jointly funded by Innovate UK and and Aquaterra. The post will be based at the company’s offices in Kintore, Aberdeen.

As a Data Scientist, your responsibility will be to gather and analyse vast volumes of complex inspection data from various sources, ensuring its quality, consistency, and relevance for further use in machine learning algorithms. You will collaborate closely with domain experts and data engineers at the company to understand the specific requirements of inspections tasks and inspection data, and utilise your expertise in data manipulation and transformation to optimize data for accurate model training.

AquaTerra is a multi-disciplined, work at height construction, inspection, repair and maintenance partner that can deliver a vast range of elements of your project safely, reliably and resourcefully. That specializes in providing innovative and sustainable offshore solutions for the energy, marine, and environmental sectors. With a strong emphasis on engineering excellence and environmental responsibility, AquaTerra offers is known for its ability to create bespoke solutions tailored to the unique challenges of each project, utilizing cutting-edge technology and a deep understanding of offshore environments. Through their commitment to safety, efficiency, and environmental stewardship, AquaTerra continues to be a leading player in advancing the offshore industry towards a more sustainable future.

You will receive extensive practical and formal training, gain marketable skills, broaden your knowledge and expertise within an industrially relevant project, and gain valuable experience from industrial and academic mentors. You will also benefit from a Personal Development Budget of £4,000.

Salary Range: 30,000- £37,500, plus £4,000 training budget.

Position Type: Full Time, Fixed Term 2 years.

Hours: 8 hours per day, on an indicative basis of 0800 – 1700 each day.

Annual Leave: 28 days paid annual leave each year including public or local holidays.

Informal enquires may be sent to: Professor Eyad Elyan at [email protected] and Martin Longmuir at [email protected]

Job Description

RESPONSIBLE TO: Martin Longmuir (Aquaterra) and Eyad Elyan (RGU)

RESPONSIBLE FOR: No Line Management Responsibilities

PURPOSE OF POST: To transfer knowledge of data science and state-of the art machine learning techniques to AquaTerra. Take a leading role in developing a set of methods for exploration, visualising, pre-processing and handling large volumes of complex inspection data and in designing, implementing, and evaluating a set of intelligent and data-driven methods for handling, processing, and analysing data captured by AquaTerra hardware facilities. Create and evaluate a range of anomaly detection methods using state-of-the-art machine learning and deep learning models. Develop a functional framework that enables real-time analytics and visualisation of inspection data collected from various sensors of Oil and Gas assets. Develop technical and personal skills (verbal and written) to meet the requirements of increasing responsibility and experience level.

PRINCIPAL DUTIES:

• Deliver the project objectives as detailed in the KTP project workplan.
• Undertake an in-depth and critical literature review in machine learning, anomaly detection, and real-time analytics of streaming data.
• Explore, understand, and critically evaluate existing technologies and practices in use at the company.
• Take a leading role in developing and evaluating the intelligent model’s anomaly detection and visualisation.
• Maintain an up-to-date project plan and provide regular progress reports.
• Deliver presentations to immediate project team members and technical experts.

Any other duties that maybe reasonable, assigned by the Academic Supervisor/ Company Supervisory teams.

Person Specifications

ESSENTIAL REQUIREMENTS

Qualifications and Professional Memberships:

Candidates must possess a First-Class Honours degree in Computing, Data Science, Machine Learning, or a strongly related discipline.

Knowledge

• Strong knowledge of data exploration, cleaning and pre-processing
• Strong knowledge of machine learning, deep learning, and the underlying theories.
• Strong knowledge and understanding of programming languages (e.g., R, Python, or similar)
• Strong knowledge of modern deep learning frameworks such as Tensorflow, PyTorch or similar

Experience

• Experience in Data science/ Machine Learning related discipline.
• Technical experience in designing, developing, and evaluating data-driven solutions using machine learning and/or deep learning frameworks
• Experience of deploying AI-based solutions locally or on the cloud

DESIREABLE REQUIREMENTS

Qualifications and Professional Memberships

Postgraduate or a PhD degree in Data Science, Machine Learning or similar field

Knowledge

• Knowledge of Deep Learning, Deep Sequence Models and Time Series Analysis

Experience

• Experience of deploying AI-based solutions locally or on the cloud
• Experience of cloud systems such as AWS, ZURE or similar platforms
• Knowledge or prior experience working in the industry sector