What Data Scientists Actually Do Day to Day

Event Overview

Curious about data science but unsure what the job really looks like beyond courses and Kaggle projects? In this live Edu AI webinar, we’ll walk through what data scientists actually do day to day—how work arrives, how it’s scoped, what “good” looks like in practice, and which tasks take up the most time. You’ll see how real-world data science blends analysis, experimentation, communication, and engineering collaboration to move a product or business metric.

We’ll map a typical week in the life of a data scientist: stakeholder meetings, defining the problem, data discovery, cleaning and validation, exploratory analysis, feature work, model training, evaluation, deployment handoff, and monitoring. We’ll also address the less-talked-about parts of the role—writing documentation, building trust in metrics, reviewing pull requests, responding to ad-hoc questions, and navigating trade-offs like speed vs. rigor. Whether you’re aiming for your first role or collaborating with data teams, you’ll leave with a clear picture of the workflows and expectations.

What you’ll learn

• The most common types of data science work: product analytics, experimentation (A/B tests), forecasting, anomaly detection, NLP/recommendations, and decision support
• How projects start: intake, scoping, success metrics, and aligning with stakeholders
• What “day-to-day” tools look like in practice: SQL, Python, notebooks, dashboards, Git, and basic MLOps concepts
• How data scientists communicate results: narratives, charts, caveats, and actionable recommendations
• How to tell the difference between Data Analyst, Data Scientist, ML Engineer, and Analytics Engineer roles
• A realistic view of time allocation: data wrangling, iteration cycles, peer review, and meetings

Who should attend

• Students and career switchers exploring data science as a path
• Early-career analysts/data scientists who want to understand industry expectations
• Product managers, founders, and marketers who collaborate with data teams
• Engineers interested in how models and analysis support product decisions

What to prepare

• Bring one role or job description you’re considering and note what seems unclear
• Come with a question about tooling, portfolios, interviews, or workplace workflows
• No coding required—optional: have a notebook/notes app ready for frameworks and checklists

The session includes a live Q&A and practical guidance on how to build a portfolio that reflects real work (problem framing, data validation, communication), not just model accuracy. Join us on Google Meet and leave with a grounded, actionable understanding of what data scientists do every day.