Data Scientist

Responsibilities

• Build and deploy data science solutions into production environments.
• Translate business problems into analytical and modelling frameworks.
• Analyse and interpret procurement and commercial data to generate actionable insights and support strategic decision-making. 
• Contribute to the delivery of end-to-end analytical projects and solutions, with guidance and support from senior team members. 
• Track, manage, and help resolve project risks, issues, and action items throughout the project lifecycle. 
• Ensure the quality, accuracy, and consistency of analytical outputs through rigorous data cleansing, transformation, modelling, and validation processes. 
• Manage priorities effectively to meet project deadlines in a fast-paced environment. 
• Collaborate closely with multidisciplinary teams worldwide, client managers, and key stakeholders to deliver high-quality analytical outputs to clients. 
• Work with client teams and senior members of DSA to develop clear, client ready presentations, both technical and non-technical. 
• Identify opportunities to improve analytical processes, automation, and reporting efficiency. 

Skills and Experience

• Strong programming skills in SQL and Python.
• Experience with database platforms such as MySQL, Amazon Redshift and Snowflake, and data science tools including Jupyter Notebook and Visual Studio Code.
• Experience applying machine learning algorithms and evaluating models.
• Strong data mining, data manipulation and analytical skills, with high attention to detail.
• Excellent written and verbal communication skills.
• Proactive self-starter with enthusiasm for data, comfortable working independently and collaboratively.
• Strong organisational skills with the ability to prioritise work and manage expectations.
• Eagerness to learn, with a positive, can-do attitude.
• Degree or equivalent in a numerical, analytical or technical subject (e.g. Mathematics, Statistics, Computer Science or Economics).

• Good understanding of statistics and data modelling concepts.
• Basic knowledge of AI concepts and practical applications.
• Familiarity with DBT, data pipelines and cloud environments.
• Experience with low-code or no-code tools (e.g. Retool).
• Experience using version control tools such as Git or GitHub.
• Familiarity with agile ways of working in a team environment.

Equal Opportunities