On a typical day you will…

• Work closely with a range of colleagues to identify, formulate and solve business problems using data.
• Explore, analyse and transform complex land and property data from a range of formats.
• Develop credible rule- or model-based methodologies to discover knowledge, extract structure from semi-, un- or poorly structured data, automate the creation of model training data, train and/or adapt ML/AI models, develop internal/external data products, data-driven tools and reports, and monitor data quality.
• Contribute to experiment design, determine suitable evaluation strategies and metrics to evaluate a range of outputs.
• Document methodologies to deliver reproducible and explainable work.
• Gather feedback and monitor the continued performance and accuracy of outputs, as applicable.
• Support development of the team by participating in team development activities, strategic discussions and communities of practice.
• Contribute to the delivery of data science projects using Agile data science methodologies.
• Build understanding of Registers of Scotland data, business processes, strategic objectives and the policy environment in which we operate.
• Communicate insights and results to technical and non-technical audiences using visualisation and data story-telling.
• Build awareness of technical and regulatory standards and developments in industry.

Please use this link here to access the role profile for this campaign.

Key Responsibilities

Essential Criteria – Skills and Attributes for Success

Essential & Technical Experience:

We will assess you against the following Experience skills during the application and assessment process:

Technical Experience

• Qualified in a quantitative field like Data Science, Computer Science, or equivalent experience.
• Proficient at problem solving using a broad range of data analysis/data science tools and approaches.
• Experience working with a range of structured, semi-structured and unstructured data types. Knowledge of graph and/or spatial data formats would be an advantage.
• Working knowledge of common Machine Learning model architectures and training processes.
• Working knowledge of using foundational models (LLMs) to create simple solutions.
• Competent in coding for data science using common Python libraries, e.g. spaCy, regex, sci-kit learn, pyTorch, numpy, pandas etc.
• Competent at SQL querying and interacting with databases using Python.
• Working understanding of ML Ops workflows.
• Some practical experience working with the AWS stack, including SageMaker.
• Demonstrates good coding practice.
• Comfortable working in a Linux environment.

Essential Experience

• Demonstrable business awareness, with an understanding of how data science can drive value and support strategic objectives.
• Familiarity with product delivery life cycles and workflow management tools.
• Ability to convey complex topics in a clear and concise manner.
• Understanding of Data and AI Governance considerations – ethics, transparency and explainability, licensing and regulation.
• Experience of working in a highly regulated sector would be an advantage.

Behaviours

At application stage, you will be scored against the bolded Behaviours and against all Behaviours for the assessment:

Changing and Improving

• Seek out and respond to feedback.
• Work with others to make improvements and implement change to team ways of working.
• Connect own work to business goals through improving understanding of business process and data.
• Continuously develop skills through continuous role and task-specific learning.

Communicating and Influencing

• Show positivity and enthusiasm towards solving complex data challenges.
• Clearly present and explain own work to stakeholders.
• Understand the business and its customers and how Data Science can deliver value to them.
• Confident in providing evidence to influence and support decisions, checking understanding of audience.

Working Together

• Work closely with a range of stakeholders including fellow data scientists, analysts, business analysts, product managers, subject matter experts, developers, ML/AI Engineers, Agile and developer coaches.
• Collaborate with others to create and use shared technical resources, tools and support.
• Contribute to formal and informal knowledge sharing sessions, communities of practice and peer review to share insight, experience and good practice.
• Build positive relationships with colleagues across teams and disciplines.

Managing a Quality Service and Delivering at Pace

• Manage work effectively in an Agile environment, focussing on stakeholder needs business priorities.
• Manage uncertainty by tracking progress, being flexible and remaining transparent about challenges.
• Apply good engineering and data science practice consistently across projects and activities.
• Document all work to ensure continuity of service.

Stage one - Application Process

To apply, click on 'Apply now' and complete the online application form.

You will need to submit:

• A CV outlining your career history and how you meet the essential criteria (max 4 pages).
• Submit your response in 3 application questions, these will assess some of the advertised behaviours and Technical Experience. Please use the STAR method (Situation, Task, Action, Result) in your examples.

When the campaign will close we issue a Hackerrank assessment invitation in order to assess the Essential & Technical Experience.

Please note:

• If we receive a high volume of applications, we may complete an initial sift on Essential & Technical Experience.
• We reserve the right to invite candidates to participate in a telephone interview prior to being further assessed.
• Applications that are not accompanied by CVs will not be scored or statements over 400 words will not be considered.
• We would strongly recommend that your statement is written in the STAR