Daintta are a rapidly growing, values-driven team of specialists who work with government clients across Cyber, Telecommunications and Data. We are seeking a talented and motivated Data Scientist to join our team and contribute to our mission of protecting the UK through data-driven insights and solutions. As a Data Scientist, you will work closely with our public sector clients and project teams to collect, analyse and interpret complex data sets, providing valuable insights that support evidence-based decision-making.

SKILLS/KNOWLEDGE

• You have 2+ years of degree level industry experience in data science.
• You have extensive degree level experience in a STEM subject.
• You have experience of working in a consultancy, engineering, or data industry.
• You have worked in client delivery across a range of projects, including data science, data analytics, data engineering, data intelligence, data security and proven experience in relevant technologies (e.g., Python applied to data science).
• You have experience working on cloud-based infrastructure (e.g., AWS, Azure, GCP).
• You have demonstrable continuous personal development.
• You have strong interpersonal skills.
• You have experience with using CI/CD tooling to analyse, build, test and deploy your code.
• You have experience in database technologies (e.g., SQL, NoSQL such as Elasticsearch and Graph databases).
• You have a good understanding of coding best practices and design patterns and experience with code and data versioning, dependency management, code quality and optimisation, error handling, logging, monitoring, validation and alerting.

• Working on client projects and providing subject matter expertise.
• Working in scrum-like environments for iterative and ‘fail-fast’ work and innovation.
• Working in cross-disciplinary teams.
• Assessing your clients’ business and technical needs with the ability to identify opportunities for data science to be used and managing clients’ stakeholders’ relationship appropriately.
• Solving problems using data science techniques and in a scientifically robust fashion.
• Identifying data sources that are relevant to client needs, and related data science concepts that leverage those sources to aid the client.
• Working with various forms of data (e.g., unstructured, semi-structured or structured; text, time-series or image) and suitably modelling them (e.g., table, key-value pair, graph) for efficient data science use.
• Investigating and analysing data to see ‘the wood from the trees’ and drilling down to the ‘whys’ of the data.
• Applying statistical and evidence-based techniques to inform insights and actions from the data.
• Communicating technical content at the right level both internally and to customers.
• Presenting to the client, using data science tooling and investigation, a ‘story’ of the data.
• Building maintainable code that use existing data science libraries, implement existing data science techniques, or implement novel techniques.
• Designing, evaluating, and implementing on-premise, cloud-based and hybrid data science and machine learning techniques and algorithms (including providing relevant review and guidance on testing aspects, identification of risks and proposing and implementing their mitigations).
• Developing scalable models and algorithms that can be deployed into production environments.
• Applying ethical principles in handling data.
• Accurately delivering high quality work to agreed timelines and taking the initiative and knowing how to `jump straight in’.
• Supporting client engagements, including pitches and presentations.
• Helping to support & grow Daintta by actively inputting into the company strategy and helping to shape our future.
• Representing us and our core values: transparent, fair and daring