Data Scientist Analyst

• →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.
• Basic understanding of agile or project-based work environments.

Hybrid, with 2-3 days working from Daintta office (London or Cheltenham) or on client site as required.

Due to clients’ requirements, all applications are strictly subject to security clearance requirements relevant to the role.