🚀 Internship

Data Science Intern

🚀 Off-cycle


💻 Remote
Rolling basis


  • You will be part of the first summer internship in the Data Science area of Data at UW. This is a 3-month internship with a start date available from mid/late June, open to people who are in their penultimate year of education (undergraduate/postgraduate) or have recently graduated.
  • This is a paid, fully-remote/hyrbrid internship opportunity where you are able to develop experience as a Data Scientist in a growing company with a modern ‘data stack’. UW has a unique word-of-mouth business model via its Partner network but is complementing that through digital channels like paid search, retargeting and referral programmes.
  • We are seeking a talented and motivated Data Science Intern to join our team for a challenging and rewarding internship. In this role, you will assist our data science team in designing, executing, and analysing A/B tests, performing causal inference analyses, and developing machine learning models. You will have the opportunity to work on real-world projects that directly impact our business, contribute to decision-making processes, and gain valuable insights into data-driven strategies.
  • Our mission with data is to create value and impact for Utility Warehouse through scaling AI and automation across our organisation. For example, we are exploring our ability to drive product growth through machine learning, understanding our customers’ needs and improving customer interactions with UW using natural language processing. We are also using machine learning models in our partners app to help equip our partners with the information they need to expand our customer base. Our exciting AI and machine learning journey is under way and we are looking for motivated individuals to join us.
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Area of Responsibilities



  • Collaborate with data scientists and stakeholders to identify key business questions and hypotheses suitable for A/B testing.
  • Design, implement, and monitor A/B tests to evaluate the impact of new features, products, or interventions on relevant business metrics.
  • Collect, clean, and preprocess large datasets for analysis using appropriate statistical and machine learning techniques.
  • Develop and apply causal inference methods, such as propensity score matching or instrumental variable analysis, to estimate causal effects and provide actionable insights.
  • Develop causal explanation of machine learning models
  • Utilise machine learning algorithms, including supervised and unsupervised techniques, to solve complex business problems and improve predictive models.
  • Perform rigorous statistical analysis and data visualisation to communicate findings effectively to technical and non-technical stakeholders.
  • Collaborate with machine learning engineers to ensure proper implementation and deployment of data science models and algorithms.
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  • Currently pursuing a degree in a quantitative field such as Statistics, Data Science, Computer Science, or a related discipline.
  • Strong foundation in statistical analysis, experimental design, and hypothesis testing.
  • Familiarity with A/B testing and causal inference methodologies and best practices.
  • Experience with machine learning algorithms and libraries (e.g., scikit-learn, TensorFlow, PyTorch).
  • Proficiency in programming languages such as Python or R.
  • Ability to work with large datasets and perform data manipulation and analysis using SQL or other relevant tools.
  • Excellent problem-solving skills and the ability to think critically.
  • Strong communication skills and the ability to present complex ideas in a clear and concise manner.
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Education requirements

Currently Studying


  • Competitive Internship remuneration.
  • Mentors and teachers aplenty; with so many creative colleagues in one place there is always someone you can lean on for support and guidance.
  • Flexible working: We’ve got fully remote teams, onsite, early risers and late-night grinders, and parents on the school run. For us, it matters what we’re working on more than where/when.
  • Work / Life balance and no-meetings day that encourage you to improve your creativity
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