Career guide

Data Scientist career guide

Use statistics and machine learning to predict, explain and optimise.

What does a data scientist do?

A data scientist uses statistics and machine learning to answer questions that simple reporting cannot: predicting demand, detecting fraud, ranking recommendations or measuring the true effect of a change.

Strong data scientists combine three things: solid statistics, practical coding, and the judgment to know when a simple model is good enough.

Typical responsibilities

Frame business problems as prediction or experimentation problems
Explore, clean and engineer features from data
Build, evaluate and tune machine learning models
Design and analyse experiments such as A/B tests
Work with engineers to deploy and monitor models
Communicate results, uncertainty and limitations

Skills you need

Technical skills

Statistics and probability
Python (pandas, NumPy, scikit-learn)
SQL
Supervised and unsupervised machine learning
Model evaluation and validation
Experiment design and A/B testing
Basics of deploying models (MLOps)

Soft skills

Framing the right problem
Communicating uncertainty honestly
Scepticism about results
Working with product and engineering teams

Tools and technologies

Python
Jupyter
scikit-learn
pandas and NumPy
SQL
PyTorch or TensorFlow
MLflow
A cloud ML platform

Certifications

IBM Data Science Professional CertificateBeginner
IBM (on Coursera)
AWS Certified Machine Learning Engineer – AssociateIntermediate
AWS · Replaces the retired AWS Machine Learning – Specialty
Google Cloud Professional Machine Learning EngineerAdvanced
Google Cloud

Certifications are optional for most roles; skills and projects matter more. Providers retire and rename exams regularly, so check the provider's website before you register. Last reviewed October 2026.

Data Scientist salary

We only publish salary ranges based on verified data, and we don't have a reliable source for this role yet. Meanwhile, if you have an offer or a target CTC, work out what you would actually take home.

In-hand salary calculator Salary hike calculator

Career path

Step 1
Associate Data Scientist
Step 2
Data Scientist
Step 3
Senior Data Scientist
Step 4
Lead or Principal Data Scientist
Step 5
Head of Data Science

Titles and the time between steps vary by company and individual.

Your first 90 days of learning

First 30 days

Statistics and Python

Revise probability, distributions and hypothesis testing
Get fluent with pandas for cleaning and exploration
Write SQL to extract and join training data

Days 31-60

Machine learning foundations

Build regression and classification models with scikit-learn
Learn cross-validation, metrics and overfitting
Analyse a mock A/B test end to end

Days 61-90

Projects and interview readiness

Complete one project from problem framing to a deployed or documented model
Explain your model's errors and limitations in writing
Practise statistics and case questions out loud

Interview preparation

Common topics to prepare for data scientist interviews. These are preparation areas, not questions from a specific company's interview.

Bias-variance trade-off and overfitting
Choosing evaluation metrics (precision, recall, AUC)
Hypothesis testing and p-values
Designing and reading an A/B test
Feature engineering and handling imbalanced data
A case: building a model for a business problem

Company interview guides Take the aptitude assessment Practice questions

Resume guidance

State the problem, the approach and the measurable result of each project
Be specific about your role in team projects
Avoid listing every algorithm; show the ones you used and why
Link notebooks or a write-up that shows your reasoning

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