Author(s): Claudia Ng

Originally published on Towards AI.

A practical guide to understanding the responsibilities, skills, and day-to-day work of the top data science roles.

I often get questions on what the different types of data science roles are from readers looking to break into the field.

The truth is that data science is a broad term that covers a wide range of roles, from analyzing business metrics to building machine learning models to deploying AI systems.

If you’re considering moving into data science or transitioning between different roles, it’s important to understand their differences so you can find the best fit for your skills and career goals.

In this post, I’ll break down 5 common roles in data science and AI, covering the key responsibilities, day-to-day tasks, required skill sets, and career paths. I’ve ordered them from more business-oriented roles to more engineering-heavy roles.

1. Product Data Scientist

Overview

Product data scientists (sometimes called data analysts) focus on defining business metrics, recommending actionable insights, and running experiments to drive product strategy.

These roles are common in tech companies in consumer apps, marketplace platforms, and SaaS companies.

Key Responsibilities

Define and track business metrics like conversion, retention, churn, and engagement.Design and analyze A/B tests to evaluate product features.Build dashboards and reports for product managers and business leaders.Conduct deep-dive analysis to inform strategic decisions.

Day-to-Day Tasks

Writing SQL queries to extract data and test hypotheses… Read the full blog for free on Medium.

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Published via Towards AI