Why Companies Look for ‘Experienced’ Data Scientists – Even for Entry-Level Jobs

The demand for data scientists has grown significantly, yet many aspiring professionals struggle to secure their first job. One of the biggest frustrations is seeing job postings for "entry-level" positions that require several years of experience. This contradiction leaves many wondering: Why do companies expect experience for a role that is supposed to be for beginners?

The answer lies in how businesses perceive data science, the challenges of hiring, and the expectations from candidates.

1. Data Science is a Business-Critical Function

Unlike many other fields, data science is directly tied to business decisions. Companies invest in data teams expecting immediate impact, not months of training. Hiring managers look for professionals who can extract insights, build models, and solve problems with minimal hand-holding.

Even at the entry level, businesses expect candidates to be familiar with real-world data challenges, such as:

• Handling messy and incomplete datasets

• Understanding business problems beyond just writing code

• Communicating insights effectively to non-technical stakeholders

This expectation often translates into requiring "experience" in working with actual business problems rather than just coursework.

2. The Definition of ‘Entry-Level’ Varies

In many industries, "entry-level" means no prior work experience is required. However, in data science, entry-level often means having practical experience, even if it’s not from a formal job.

Companies expect candidates to have:

• Hands-on projects demonstrating applied skills

• Internships or freelance work showcasing real-world impact

• Participation in competitions (like Kaggle) or open-source contributions

From an employer’s perspective, these experiences differentiate someone who has only learned concepts from someone who can apply them in practical scenarios.

3. The Skills Gap Between Academia and Industry

Many data science courses, bootcamps, and degrees focus on theoretical knowledge but lack industry-aligned training. Most fresh graduates are skilled in machine learning models but struggle with:

• Deploying models into production

• Understanding data engineering and ETL processes

• Working with cloud platforms and big data tools

• Writing efficient, scalable code

Since companies prefer candidates who can contribute from day one, they favor those who have bridged this gap through self-learning, internships, or side projects.

4. Hiring Junior Data Scientists is Risky for Companies

Businesses hesitate to hire completely inexperienced data scientists because:

• The cost of hiring and training is high. If a candidate takes months to get up to speed, the company loses valuable time and resources.

• There’s uncertainty in the ROI. Unlike software engineering, where junior developers can be assigned simpler tasks, data science projects often require a deeper understanding of domain knowledge, business impact, and model interpretability.

• They need candidates who can work independently. Startups and mid-sized companies often lack the resources to provide extensive mentorship, making experienced candidates more attractive.

5. How to Overcome the ‘Experience’ Barrier

While breaking into data science may seem difficult, there are ways to build experience without having a formal job.

A. Work on End-to-End Projects

Building models is not enough. Companies want to see projects that include:

• Data collection, cleaning, and preprocessing

• Exploratory data analysis with meaningful insights

• Model deployment and real-world application

Sharing well-documented projects on GitHub and writing case studies on platforms like Medium or LinkedIn can make a significant impact.

B. Gain Practical Exposure Through Internships and Freelancing

Even unpaid internships or freelance projects can provide valuable hands-on experience. Platforms like Upwork, Fiverr, and Kaggle consulting challenges can help gain industry-relevant exposure.

C. Contribute to Open Source and Participate in Competitions

Contributing to open-source data science projects or participating in Kaggle competitions helps demonstrate problem-solving skills while gaining practical knowledge.

D. Network and Showcase Your Work

Engaging with professionals on LinkedIn, attending industry webinars, and sharing personal projects can attract potential employers. Hiring managers are more likely to consider a candidate who actively demonstrates their expertise.

The expectation of experience in entry-level data science roles is not necessarily about prior employment but about demonstrating the ability to solve real-world problems. Instead of focusing on job titles, aspiring data scientists should focus on gaining practical experience, building a strong portfolio, and showcasing their problem-solving skills.

Stay tuned to learn more in this space.