About the role
As a Data Science Intern at Wiviy, you'll investigate datasets to uncover patterns, test hypotheses and develop analytical approaches to business and product questions. You'll support data exploration, statistical analysis and predictive modelling while learning how evidence can guide product decisions. Your work will help turn complex information into useful insights.
What you'll do
- Explore datasets to identify patterns and meaningful relationships.
- Perform data cleaning, transformation and exploratory analysis.
- Apply statistical methods to investigate defined business questions.
- Build and evaluate predictive models under team guidance.
- Visualise findings and communicate analytical conclusions clearly.
- Document methodologies, assumptions and experiment results.
What you'll bring
- Understanding of Python and foundational programming concepts.
- Basic knowledge of statistics, probability and analytical methods.
- Familiarity with Pandas, NumPy or similar data libraries.
- Ability to clean, explore and interpret structured datasets.
- Interest in predictive modelling and evidence-based problem-solving.
- Clear documentation and communication skills.
Nice to have
- Experience with Scikit-learn or other modelling libraries.
- Familiarity with SQL and data visualisation tools.
- Academic or personal projects involving real-world datasets.
Who you'll work with
You'll work with Wiviy's data, product and engineering teams to frame analytical questions and explore relevant datasets. You'll share methods and findings with stakeholders, helping connect statistical analysis and modelling work to practical product or business decisions.
Why this role matters
Data science helps teams move beyond assumptions and understand what the evidence reveals. Your work will help Wiviy explore user and business patterns, test ideas and identify opportunities to make product decisions more informed and effective.
Salary Range
Competitive stipend based on experience and role scope.
Compensation may vary based on experience, scope, location and other role-related factors.