Data Scientist

Daintta is a rapidly expanding, values-driven team of specialists who collaborate with both public and private sector clients across the domains of Cyber, Telecommunications, and Data. We are seeking a talented and motivated Cyber Security Graduates to join our team and contribute to our mission of safeguarding the UK through data-driven insights and solutions.

Being a part of our Advanced Professions Programme means you'll be at the heart of our consulting projects - supporting senior consultants, engaging with clients, and helping to deliver solutions to their most complex challenges. This is an exciting opportunity to launch your career in consulting, gain exposure to different industries, and develop your skillset in a fast-paced, collaborative and supportive environment.

As a Data Scientist Analyst at Daintta, you will contribute to real-world data projects by exploring and analysing diverse datasets, applying statistical techniques, and supporting the development of scalable models. You'll gain hands‑on experience with industry‑standard tools, learn to communicate insights effectively, and build a strong foundation in ethical and practical data science all while working alongside experienced professionals in a dynamic, client‑focused setting.

• Support client projects under the guidance of senior data scientists.
• Work in agile, iterative environments that encourage experimentation and learning.
• Assist in understanding client business and technical needs to identify opportunities for data science.
• Apply basic data science techniques to solve problems in a structured and robust way.
• Help identify and prepare relevant data sources for analysis.
• Work with various types of data (structured, semi‑structured, unstructured) and learn appropriate modeling approaches.
• Explore and analyse data to uncover patterns and insights.
• Use statistical methods to support evidence‑based decision‑making.
• Communicate technical findings clearly to both internal teams and clients.
• Contribute to presenting data‑driven stories using appropriate tools and visualisations.
• Write maintainable code using established data science libraries and techniques.
• Learn to support the design and implementation of machine learning models in different environments (cloud, on‑premise, hybrid).
• Assist in developing scalable models that can be deployed in production settings.
• Follow ethical guidelines when handling and analysing data.
• Deliver high‑quality work within agreed timelines and show initiative in learning and contributing.
• Support client engagements, including preparing materials for presentations and meetings.
• Contribute to internal initiatives that support the growth of the data science practice.
• Uphold and promote our values of being transparent, fair, and daring, both internally and externally.

• Degree Educated or equivalent.
• Communication:
  Clear verbal and written communication, especially when translating technical concepts for non‑technical audiences.
• Collaboration:
  
  Ability to work effectively in multidisciplinary teams and contribute to shared goals.
• Problem‑Solving:
  Analytical thinking and a structured approach to breaking down complex challenges.
• Adaptability:
  Willingness to learn new tools, frameworks, and methodologies quickly.
• Initiative:
  Proactive attitude, with the confidence to ask questions and take ownership of tasks.
• Time Management:
  Ability to manage multiple tasks and meet deadlines in fast‑paced environments.
• Attention to Detail:
  Careful and thorough approach to analysis, documentation, and delivery.
• Client Focus:
  Professionalism and empathy when engaging with stakeholders and understanding their needs.

• Exposure to machine learning concepts, such as classification, clustering, or model evaluation.
• Experience with data visualisation tools like Matplotlib, Seaborn, Power BI, or Tableau.
• Familiarity with cloud platforms (e.g. AWS, Azure, or GCP) or version control tools like Git.
• Knowledge of ethical considerations in data science, such as bias, fairness, and responsible AI.
• Experience working with unstructured data, such as text or images.
• Bas…