Trainee Decision Scientist

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Skills you'll use

What you'll do

• Explore large datasets to surface patterns, relationships and modelling opportunities
• Build, train and evaluate machine-learning models for real product and business problems
• Engineer features, handle missing data and build reproducible data pipelines
• Design and analyse A/B tests and quantify the impact of product changes
• Validate models for accuracy, bias, drift and generalisation before rollout
• Work with engineering to move models from notebook to production
• Visualise and present results so non-technical stakeholders can act on them
• Document experiments, assumptions and results for reproducibility

Who can apply

• Bachelor's/Master's degree in Data Science, Statistics, Mathematics, Computer Science or a related quantitative field
• 0–2 years of professional experience — 2023–2026 graduates and freshers with strong project work are encouraged to apply
• Strong Python for data work — pandas, NumPy, scikit-learn, matplotlib/seaborn
• Solid statistics: probability, distributions, hypothesis testing, regression
• Understanding of core ML — supervised vs unsupervised, train/test splits, cross-validation, overfitting
• Good SQL for extracting and joining data from warehouses
• Familiarity with notebooks, Git and reproducible analysis practice
• Bonus: exposure to deep learning (TensorFlow/PyTorch), NLP or recommendation systems
• Ability to frame a business problem as a measurable modelling problem
• Clear communication and intellectual honesty about what the data does and does not show

Perks & benefits

Interview & selection process

6 rounds — typical selection flow for this role:

1. 1Resume & profile screening
2. 2Online assessment — statistics, Python and SQL
3. 3Technical interview — ML fundamentals and past projects
4. 4Case round — framing and solving a data problem
5. 5HR discussion & culture fit
6. 6Offer letter

About Mu Sigma

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