Data Scientist

Unipart Technologies Group; Leeds; On-site; GBP 40,000 - 80,000; Full time

Job description

Instrumentel is a world-leading provider of asset monitoring solutions for precision measurement in extreme environments. Our solutions include Condition-Based Predictive Maintenance, Condition Monitoring of remote assets and predictive analytics to improve asset performance.

We are now looking to recruit a Data Scientist to join our busy team in Leeds. As a Data Scientist at Instrumentel, you will leverage your analytical skills and technical expertise to develop innovative data-driven solutions. You will work with cross-functional teams to extract meaningful insights from complex datasets, build robust machine learning models, and deploy scalable solutions that address critical business challenges across a variety of industries, including rail, manufacturing, sustainability, and FMCG.

Key responsibilities of the role will include:

• Data Analysis and Modeling: Develop and apply advanced statistical and machine learning techniques to analyze large and complex datasets.
• Feature Engineering: Extract meaningful features from diverse data sources, including time-series, sensor data, and image data.
• Model Development and Deployment: Build, train, and deploy predictive models to identify patterns, anomalies, and actionable insights.
• Machine Learning Operations (MLOps): Collaborate with teams to implement robust MLOps pipelines for model deployment, monitoring, and retraining.
• Data Visualisation / Story Telling: Create clear and impactful data visualisations to communicate findings to both technical and non-technical audiences, using methods such as data story telling and interactive communication tools (i.e. plotly)
• Machine Vision: Apply computer vision techniques, e.g. object detection, instance segmentation, OCR, to analyse images and videos, enabling the detection of defects, anomalies, and operational inefficiencies.
• Domain Expertise: Collaborate with industry experts and customers to gain a deep understanding of business challenges and requirements, and translate them into actionable data-driven solutions.
• Innovation and Research: Stay up-to-date with the latest advancements in data science and machine learning, and contribute to research and development initiatives.
• Collaboration: Work closely with cross-functional teams, including engineers, product managers, and domain experts, to deliver impactful solutions.

The successful candidate will be qualified to Degree level in Mathematics, Computer Science, Statistics, Physics, or a similarly quantitative field. Candidates must also be able to demonstrate the following skills and experience

• Experience in statistical modeling, machine learning, data mining, unstructured data analytics, natural language processing.
• Proficiency in statistical and other tools/direct coding languages, e.g., Python, R, SQL.
• Familiarity with relational databases and intermediate-level knowledge of SQL.
• Sound understanding of a wide range of statistical techniques.
• Advanced analytical techniques, e.g., regression analysis, predictive analysis, data mining.
• Agile Methodologies.
• Experience in data mining/feature selection.
• Understanding of machine learning/predictive modeling.
• Excellent communication skills (both written and verbal).
• High self-motivation and ability to work autonomously.
• Excellent interpersonal skills dealing with stakeholders at all levels within the organization.
• Experience with computer vision techniques (e.g., image classification, object detection).
• Ability to work with large datasets and complex data pipelines.
• Ability to translate business requirements into technical solutions.

In addition to the above experience, candidates must be able to demonstrate a strong analytical mind and a keen eye for detail, the ability to work independently and as part of a team, a passion for learning and staying up-to-date with the latest trends in data science and machine learning and a strong work ethic with a commitment to delivering high-quality work.