Entry Level Data Science Jobs: What to Expect and How to Get Started in 2024

With employment for data scientists expected to grow by 35% from 2022 to 2032 in the US, they play an essential role in companies extracting valuable insights from data. From sourcing data to deploying machine learning models, data scientist jobs vary widely depending on experience and responsibilities.

In this article, we’ll explore the entry-level data science jobs in 2024 that you can secure without extensive experience or a decorated resume.

1. Junior Data Scientist

A junior data scientist plays a supporting role in data analysis and machine learning projects. While they may not be as experienced or may not enjoy the same level of autonomy as a senior data scientist, they’re expected to contribute significantly to the team’s goals.

Responsibilities

As a junior data scientist, your responsibilities are similar to those of senior data scientists, except for spearheading complex projects.

They include:

• Data preprocessing, cleaning, and wrangling
• Assisting in building and evaluating machine learning models
• Conducting exploratory data analysis (EDA) and feature engineering
• Supporting data-driven decision-making with statistical analysis
• Collaborating with senior data scientists and engineers on model deployment and optimization

Salary

The average base salary for a junior data scientist in the US is around $87,000-90,0000.

Basic Skills Requirements

As your upward mobility features senior data scientist positions, basic skill requirements remain aligned with those roles. Proficiency in Python or R and a firm grasp of statistics and probability are essential. Familiarity with machine learning algorithms, such as regression and classification, is also important.

Moreover, experience with data manipulation libraries like pandas and NumPy, coupled with an understanding of SQL for data querying, is critical for success as a junior data scientist. Additionally, a basic knowledge of data visualization tools may help you get noticed faster.

2. Data Science Intern

Data science internships offer valuable hands-on experience in the practical aspects of the industry and can be a great way to develop skills and knowledge in the field. A data science intern usually works on projects that involve collecting, cleaning, analyzing, and interpreting data.

Responsibilities

As junior data scientists, the data science intern role is also a subset of data scientist positions. Responsibilities include:

• Assisting in data collection, cleaning, and preprocessing
• Performing exploratory data analysis (EDA) to identify trends
• Supporting model development and testing
• Creating basic data visualizations and reports
• Collaborating with senior data scientists on ongoing projects

Salary

The average data science intern salary ranges from $15–$25 per hour, depending on the specific role and location.

Basic Skills Requirements

While you may not lead critical data science projects, you might be asked to contribute to essential components. Efficiently tackling these issues requires understanding statistical concepts and EDA, proficiency in programming languages like R and Python, and robust analytical and problem-solving skills. Data visualization skills, invariably, are critical for this role as well.

Starting your career as an entry-level data scientist is an exciting opportunity, but it also comes with its fair share of technical and professional challenges. These challenges are essential for you to recognize your ability and navigate the office environment before progressing to a role with more responsibilities. Some of these include:

Managing Expectations

For instance, your manager may expect a deep learning model to be developed and deployed in two weeks. As an entry-level data scientist, you might confidently agree to the timeline.

However, as you delve into the project, due to inexperience, you may encounter complex data preprocessing issues and difficulties with hyperparameter tuning—stretching the initial timeline and revealing your misjudgment.

Clear communication with your manager often resolves issues. Make sure their expectations are realistic early on.

Balancing Theory and Practical

There’s often a gap between theoretical knowledge and practical application in a business context. For example, we learn that linear regression assumes a linear relationship. However, when applying it to real-world data, it often performs poorly due to non-linear relationships.

Focus on applying theory to real-world problems while preparing for the interview, and learn from mistakes. Engage in projects that allow you to bridge this gap and gain practical experience.

Data Cleaning and Preprocessing

Raw data is often messy, incomplete, and inconsistent. Cleaning and preprocessing the data can be time-consuming and requires a deep understanding of the data, which you may not yet have as an entry-level data scientist.

Develop strong skills in data wrangling and become proficient in tools and libraries like pandas in Python. However, a practical understanding of the domain is more critical for making informed decisions about handling raw data than theoretical practices.

Selecting the Right Model

With many ML algorithms and models to choose from, it can be challenging to select the right one for a given problem. Gain a solid understanding of different machine learning algorithms, their assumptions, and their appropriate use cases.

Junior data scientists, for instance, often choose a complex neural network model to predict customer churn, thinking it will yield the best results. However, after struggling with overfitting and long training times, they often switch to a simpler logistic regression model, which performs better and is easier to interpret.

Handling Large Datasets

The large datasets you worked with during preparation will feel small compared to the vast data lakes you’ll encounter in a real data science job. Moreover, working with those huge datasets can be computationally expensive and slow, especially if you’re not familiar with optimization techniques. Learn about efficient data handling techniques, such as using vectorized operations, batching, and leveraging distributed computing frameworks like Apache Spark or Dask.

Collaborating and Communication

One of our candidates, a data science intern, was tasked with helping the marketing team on a customer segmentation project. Excited to showcase their skills, they built a complex clustering model without regularly consulting the team. When they finally presented the results, the segments were too abstract and didn’t align with the team’s needs.

Collaborating with engineers, product managers, and other stakeholders requires effective communication and teamwork, which can be challenging if you’re not used to it.

Develop strong interpersonal skills, be open to feedback, and learn to communicate your ideas effectively. Understanding the perspectives and needs of other team members can lead to better collaboration.

Virtually, no gap exists between the skills required for a senior-level data science position and a junior-level one. The specific skill requirements, however, may vary depending on your company and the particular role you’re interested in. Here are some of the basic skills that an entry-level data science position may expect from you in 2024:

Programming Languages

Python is among the most popular programming languages in data science, followed by R. Learning and effectively applying your programming language skills are critical for data manipulation, analysis, and modeling tasks assigned to you as an entry-level data scientist. If you lack advanced programming knowledge, you may start with Python Learning Path for its extensive libraries and ease of use.

SQL Querying

As a data scientist interview candidate, entry-level or otherwise, database management and extraction are critical skills required to succeed in your role. Therefore, refining your SQL database manipulation skills is essential to crack entry-level data science jobs in 2024. Explore our SQL interview questions and SQL Learning Path to prepare well for complex queries.

Data Visualization Tools

Presenting your insights and generating reports are key parts of data science roles. Experience with tools like Tableau, Power BI, or Matplotlib (Python) for creating visualizations that help in interpreting and presenting data insights.

Cloud Platforms

For data scientists, cloud platforms are particularly valuable because they provide access to powerful computing resources and specialized tools for data analysis and machine learning.

As most data lakes are reposited on cloud platforms like AWS, GCP, or Azure, you must be aware of how these platforms work and how to safely access data from these repositories.

Machine Learning Frameworks

Data science and forecasting require massive amounts of data and machine learning models to find patterns. Therefore, understanding fundamental machine learning algorithms and concepts, such as regression, classification, and clustering, is critical for an entry-level data science candidate.

Problem-Solving Abilities

As a data scientist, you’ll be expected to critically approach real-life business complexities and present solutions. Strong analytical thinking and problem-solving abilities to tackle data-related challenges and derive actionable insights are critical to success.

Communication Skills

As mentioned, the ability to effectively communicate findings and insights to both technical and non-technical stakeholders, often through reports or presentations, is a soft-skill requirement for entry-level data science candidates in 2024.

The field of data science continues to grow rapidly, offering a wide range of opportunities for fresh graduates. Entry-level positions, such as junior data scientists and data science interns, provide a solid foundation for building a career in this exciting field.

To succeed in these roles, navigating the initial challenges and developing a strong foundation in technical skills like programming, statistics, and machine learning is essential. Additionally, soft skills such as problem-solving and communication are necessary for effectively working with data and collaborating with your potential colleagues.

All the best!