Data Science Professional in Training (PIT)

Brief job Description

Working closely with the senior team, the Data Science PIT will contribute to data exploration, statistical analysis, machine learning, visualisation, and the development of data products and minimal viable products (MVPs). The role provides hands-on exposure to consulting, data science, AI, cloud technologies, and emerging analytical tools within a dynamic and impact-driven environment.

This role is ideal for a curious, analytical, and self-motivated individual who enjoys solving problems with data, is passionate about technology and continuous learning, and is excited to apply data science to real-world business challenges.

• Data Analysis & Insights
  

• Gain a clear understanding of client business challenges and identify how data can be used to address them.
  

• Collect, clean, transform, and prepare structured and unstructured data for analysis.
  

• Conduct exploratory data analysis to identify trends, patterns, relationships, and anomalies.
  

• Apply statistical and analytical techniques to generate meaningful business insights.
  

• Translate complex analytical findings into clear, actionable recommendations for clients and internal stakeholders.
  

• Support the development of reports, dashboards, presentations, and data-driven narratives.
  

• Data Science & Machine Learning
  

• Develop, test, and evaluate predictive and machine learning models to address business problems.
  

• Apply appropriate statistical, mathematical, and machine learning techniques to different use cases.
  

• Support model training, validation, optimisation, and performance monitoring.
  

• Work with machine learning frameworks and libraries such as Scikit-learn, TensorFlow, or similar tools.
  

• Identify opportunities to apply AI and advanced analytics to improve business processes and decision-making.
  

• Document methodologies, assumptions, results, and model limitations.
  

• Data Visualisation & Reporting
  

• Develop clear and intuitive data visualisations to communicate insights effectively.
  

• Build and maintain dashboards using tools such as Power BI, Tableau, or similar platforms.
  

• Identify the most appropriate visualisation techniques for different audiences and business questions.
  

• Present analytical findings to both technical and non-technical stakeholders.
  

• Support the creation of client-ready data reports and presentations.
  

• Data Engineering & Solution Development
  

• Support the development of data pipelines and processes for collecting, transforming, and integrating data.
  

• Work with databases and query languages such as SQL to extract and manipulate data.
  

• Assist in integrating data from multiple sources to create reliable datasets for analysis.
  

• Contribute to the development of data science MVPs and analytical solutions.
  

• Apply version control and software development practices to analytical and data science projects.
  

• Ensure data solutions are scalable, reproducible, and appropriately documented.
  

• AI, Emerging Technology & Tool Evaluation
  

• Research emerging data science, AI, and analytics tools and technologies.
  

• Evaluate new tools, platforms, frameworks, and methodologies for potential application within client projects.
  

• Experiment with generative AI, predictive analytics, automation, and other emerging technologies where relevant.
  

• Recommend improvements to existing analytical approaches and data solutions.
  

• Document and share learnings on the effectiveness and applicability of different technologies.
  

• Data Quality, Governance & Responsible AI
  

• Apply data quality checks and identify inconsistencies, gaps, and potential sources of bias.
  

• Support the implementation of appropriate data governance and management practices.
  

• Handle data responsibly and maintain confidentiality when working with client and organisational information.
  

• Consider ethical, privacy, security, and fairness implications when developing data and AI solutions.
  

• Ensure analytical outputs are transparent, explainable, and appropriately validated.
  

Requirements

• Degree/diploma in Data Science, Statistics, Mathematics, Computer Science, Information Systems, Engineering, Economics, or a related quantitative field.
  

• Experience with at least one programming language, preferably Python or R is advantageous
  

• Familiarity with SQL and relational databases.
  

• Familiarity with data analysis and visualisation tools such as Power BI, Tableau, Python, or similar platforms.
  

• Understanding of statistical analysis, machine learning, and predictive modelling.
  

• Exposure to cloud environments such as AWS or Microsoft Azure is advantageous.
  

• Understanding of data engineering, data governance, or MLOps practices is beneficial.
  

Benefits

Company Benefits:

• Complimentary snack bar
  

• Individualised learning and development opportunities
  

• Project Experience- opportunity to lead and find impactful solutions for clients
  

• Paid study leave
  

• Hybrid work (post probation)
  

• 4 day work week (post probation)