The difference between data scientist and software engineer roles is often a topic of curiosity for those exploring careers in tech.

While both positions involve working with technology and data, they serve distinct purposes and require unique high income skills.

In this article, we’ll delve into the responsibilities and opportunities associated with these two dynamic jobs. We will help you decide which path aligns with your interests and personal goals for work.

What is a data scientist?

A data scientist is a professional who analyzes and interprets complex information to help organizations make informed decisions.

They work at the intersection of mathematics, statistics, programming, and domain expertise, leveraging their skills to extract valuable insights from large and often unstructured datasets.

What does a data scientist do?

    1. Data Collection and Cleaning
      - Gathering information from various sources such as databases, APIs, or external files.
      - Cleaning and preprocessing data to ensure accuracy and consistency for analysis.

    2. Exploratory Data Analysis (EDA)
      - Analyzing data to identify trends, patterns, and anomalies.
      - Creating visualizations to summarize and communicate findings effectively.

    3. Statistical and Machine Learning Modeling
      - Applying statistical methods to identify relationships within the data.
      - Building and deploying machine learning models to predict outcomes or automate processes.

    4. Interpreting Results
      - Translating complex data insights into actionable recommendations for stakeholders.
      - Communicating findings through visualizations, reports, and presentations.

    5. Collaboration
      - Working with business teams to define problems and align data solutions with organizational goals.
      - Collaborating with data engineers to ensure efficient pipelines and infrastructure.

How to become a data scientist?
  1. Learn Python, R, and SQL for data manipulation and analysis.
  2. Master statistical methods and algorithms like regression, decision trees, and neural networks.
  3. Use tools like Tableau, Power BI, Matplotlib, and Seaborn to present insights.
  4. Work on real-world datasets, build projects, and participate in competitions like Kaggle.
  5. Understand the industry you want to perform in (e.g., finance, healthcare).
  6. Showcase your projects on GitHub or through interactive dashboards.
  7. Join communities, attend meetups, and consider certifications like Google or IBM Data Science programs.

Typical industries:

  • Technology: Optimizing user experiences and personalizing content.
  • Finance: Fraud detection and risk analysis.
  • Healthcare: Predicting patient outcomes and improving diagnostics.
  • Retail: Enhancing supply chain efficiency and customer personalization.

Data scientist resume example

Daniel Lee
San Francisco, CA
daniel.lee@email.com | (555) 123-4567

Summary

Data Scientist with experience using statistical modeling, machine learning, and data engineering to drive business impact. Proven ability to translate complex data into actionable insights, improve product performance, and build scalable data pipelines.

Professional Experience

Senior Data Scientist

Netflix – Los Gatos, CA

March 2021 – Present

  • Develop and deploy a collaborative filtering recommender model using matrix factorization, improving user engagement with personalized content by 22%.
  • Create a churn prediction model that reduced customer attrition by 17%, enabling targeted retention campaigns.
  • Led cross-functional data projects with content and product teams, delivering insights that influenced $10M+ in quarterly content spend.

Data Scientist

Airbnb – San Francisco, CA

June 2018 – February 2021

  • Built a dynamic pricing algorithm for Airbnb hosts, resulting in an average 14% increase in nightly revenue across pilot markets.
  • Performed cohort and funnel analysis to identify drop-offs in the booking flow, increasing conversion rates by 11% after UX optimizations.
  • Partnered with the infrastructure team to improve ETL processes and reduce data latency from 4 hours to 15 minutes.

Data Analyst (Intern → Full-Time)

Spotify – New York, NY

July 2016 – May 2018

  • Created real-time dashboards in Tableau and Looker for the marketing team to track campaign KPIs and user acquisition metrics.
  • Conducted retention and segmentation analysis of new premium users using RFM modeling, increasing re-engagement by 20%.
  • Supported A/B testing initiatives by defining metrics, analyzing results, and presenting insights to senior leadership.

Education

Master of Science in Data Science

Columbia University – New York, NY

Graduated: May 2016

Bachelor of Science in Statistics & Computer Science

University of California – Berkeley, CA

Graduated: May 2014

Skills

  • Languages & Tools: Python (pandas, scikit-learn, PyTorch), SQL, R, Spark, Airflow, Docker
  • Cloud Platforms: AWS (S3, Redshift, SageMaker), GCP (BigQuery)
  • Data Visualization: Tableau, Looker, matplotlib, seaborn
  • Modeling: Supervised & Unsupervised Learning, NLP, Time Series, Recommender Systems
  • Other: A/B Testing, Experimental Design, ETL, Data Pipelines, Git

Certifications

  • AWS Certified Machine Learning – Specialty
  • Deep Learning Specialization – Coursera (Andrew Ng, Deeplearning.ai)

What is a software engineer?

A software engineer is a professional who designs, develops, tests, and maintains systems to solve real-world problems or meet business needs.

They apply principles of computer science and engineering to build applications, systems, and tools that are efficient, reliable, and scalable.

What does a software engineer do?

    1. Software Design and Development
      - Designing software architecture and creating technical specifications.
      - Writing clean, maintainable, and efficient code to implement functionality.

    2. Testing and Debugging
      - Identifying and resolving bugs or performance issues.
      - Writing automated tests to ensure the program works as intended.

    3. System Integration
      - Ensuring different components of software work seamlessly together.
      - Collaborating with other teams to integrate third-party tools or APIs.

    4. Maintenance and Updates
      - Monitoring performance and making improvements over time.
      - Adding new features or modifying existing ones based on user feedback or changing business needs.

    5. Collaboration and Communication
      - Working with product managers, designers, and other developers to understand requirements.
      - Participating in code reviews and team discussions to maintain quality.

How to become a software engineer?
  1. Master programming languages like Java, Python, C++, or JavaScript. Practice coding regularly through exercises and projects.
  2. Learn the fundamentals of data structures (e.g., arrays, trees) and algorithms (e.g., sorting, searching) to solve problems efficiently.
  3. Build projects, contribute to open-source software, or do internships to gain practical experience.
  4. Familiarize yourself with version control (Git), integrated development environments (IDEs), and frameworks (e.g., React, Django).
  5. Practice coding challenges on platforms like LeetCode, HackerRank, or CodeSignal to improve your problem-solving abilities.
  6. Follow industry trends, new programming languages, and technologies to stay current in the field.
  7. Showcase your projects and contributions on platforms like GitHub to demonstrate your skills to potential employers.

Typical industries:

  • Technology: Developing apps, operating systems, and cloud services.
  • Finance: Building secure payment systems and financial tools.
  • Healthcare: Creating software for medical devices or patient management systems.
  • E-Commerce: Designing scalable online platforms and personalized user experiences.

Software engineer resume example

Emily Chen
San Francisco, CA
emily.chen@email.com | (555) 678-1234

Summary

Full-stack Software Engineer with experience building scalable web applications, RESTful APIs, and cloud-native services. Proficient in JavaScript, Python, and modern frameworks such as React and Node.js.

Professional Experience

Senior Software Engineer

Stripe – San Francisco, CA

May 2021 – September 2025

  • Designed and implemented scalable APIs for Stripe’s Billing product, reducing latency by 35% and supporting 500K+ requests/day.
  • Led migration of legacy services to a microservices architecture using Go and gRPC, increasing team deployment velocity by 40%.
  • Mentored 3 junior engineers and led weekly review sessions to uphold best practices in system design and code quality.
  • Integrated internal developer tools to enhance CI/CD pipelines, reducing average build times by 25%.

Software Engineer II

Google – Mountain View, CA

August 2017 – April 2021

  • Contributed to the development of Google Calendar’s web front-end using TypeScript and React, increasing load performance by 20%.
  • Built internal testing tools that reduced regression bugs by 30% and streamlined QA processes across multiple teams.
  • Collaborated with UX researchers to A/B test new UI features, leading to a 12% increase in weekly active users.
  • Participated in the company-wide initiative to adopt Web Components for UI consistency across Google Workspace products.

Software Engineering Intern

Dropbox – San Francisco, CA

Summer 2016

  • Developed a prototype for Dropbox Paper’s collaborative editing feature using Node.js and WebSockets.
  • Implemented caching logic using Redis that improved document load speed by 18%.
  • Presented project to the engineering leadership team and earned a full-time return offer.

Education

Bachelor of Science in Computer Science

University of California – Berkeley, CA

Graduated: May 2017

Skills

  • Languages: JavaScript, TypeScript, Python, Go, SQL
  • Frameworks: React, Node.js, Express, Django, Flask
  • Tools & Platforms: Docker, Kubernetes, Git, Jenkins, Redis, PostgreSQL, GraphQL
  • Cloud Services: AWS (Lambda, S3, EC2), Google Platform (App Engine, Functions)
  • Practices: Agile/Scrum, TDD, CI/CD, Code Review, RESTful API Design

Certifications

  • AWS Certified Solutions Architect – Associate
  • Frontend Masters Advanced React Course – Completed 2023

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Software engineer vs data scientist

Education

When comparing the academic paths, there are some key differences. However, there is also overlap in skills and concepts.

Data scientist

1. Bachelor's Degree:

  • Field: Data Science, Computer Science, Statistics, Mathematics, Engineering, or Physics.
  • Focus: They often start with a strong foundation in mathematics, statistics, and programming.
  • Skills Learned:
    • Data analysis
    • Programming (Python, R)
    • Machine learning algorithms
    • Statistical modeling
    • Data visualization tools (e.g., Tableau, Power BI)

2. Master's Degree (optional but common):

  • Field: Data Science, Artificial Intelligence, Machine Learning, or a related field.
  • Focus: Deeper knowledge of machine learning, big data, and advanced statistical methods.
  • Skills Learned:
    • Deep learning
    • Big data technologies (Hadoop, Spark)
    • Advanced machine learning algorithms
    • Natural language processing

3. Ph.D. (optional for research roles or specialized fields):

  • Field: Statistics, Data Science, Computer Science, or Artificial Intelligence.
  • Focus: Advanced research in specific areas such as AI, NLP, or deep learning.
Software engineer

1. Bachelor's Degree:

  • Field: Computer Science, Software Engineering, Information Technology, or related fields.
  • Focus: Software Engineers typically have a strong foundation in algorithms, programming, and system design.
  • Skills Learned:
    • Programming (Java, Python, C++, etc.)
    • Data structures and algorithms
    • Software development principles
    • Object-oriented programming (OOP)
    • Operating systems and databases

2. Master's Degree (optional but beneficial for specialized roles):

  • Field: Software Engineering, Computer Science, or Cybersecurity.
  • Focus: More advanced topics in software, system architecture, or specialized technologies like cloud computing or mobile app development.
  • Skills Learned:
    • Distributed systems
    • Cloud technologies (AWS, Azure)
    • Software architecture and design patterns
    • Agile development practices

3. Certifications and Bootcamps:

  • Many Software Engineers enhance their skills through certifications or coding bootcamps.
  • Common certifications: AWS Certified Solutions Architect, Microsoft Certified: Azure Developer Associate, Google Cloud Professional Cloud Architect.
  • Bootcamps: These are shorter programs focusing on coding skills, offering hands-on practice and project-based learning.

Data scientist vs software engineer salary

Here’s an overview of the pay differences between data scientists and software engineers at different experience levels. Salaries can vary based on location, company, and industry, but this gives a general idea.

Experience LevelData ScientistSoftware Engineer
Entry-Level$70,000 - $90,000$70,000 - $95,000
Mid-Level$90,000 - $120,000$95,000 - $130,000
Senior-Level$120,000 - $160,000$130,000 - $180,000
Lead/Principal$150,000 - $200,000$160,000 - $220,000

Factors affecting salary:

  • Location: Salaries are often higher in tech hubs like San Francisco, New York, or London.
  • Company: Large tech companies tend to offer higher pays and more benefits.
  • Education & Skills: Data scientists with advanced degrees or specialized knowledge (e.g., in AI/ML) can earn more, as can software engineers with expertise in high-demand languages or frameworks.

In general, data scientists tend to start with slightly lower salaries than software engineers but can catch up as they move into senior or specialized roles.

Other differences

AspectSoftware EngineerData Scientist
Primary FocusDeveloping, testing, and maintaining software applications.Analyzing data to extract insights and build models.
Skills RequiredProficiency in programming languages (e.g., Java, C++, Python), algorithms, and system design.Expertise in statistics, machine learning, data manipulation, and programming (usually Python, R).
Tools UsedIntegrated Development Environments (IDEs), version control, databases, cloud platforms.Tools like Python libraries (Pandas, NumPy), SQL, machine learning frameworks (e.g., TensorFlow).
Typical ProjectsBuilding software applications, designing user interfaces, creating backend systems.Data analysis, predictive modeling, machine learning algorithms, statistical testing.
Work InvolvementFocus on technical execution, coding, and implementation.Focus on understanding business problems through data and building models to solve them.
End GoalDeliver functional software products.Extract meaningful insights from data to drive decision-making.
CollaborationWorks closely with designers, product managers, and other engineers.Works with data engineers, business analysts, and sometimes directly with stakeholders.
Job ToolsGit, Docker, Kubernetes, Jenkins, IDEs like Visual Studio, IntelliJ.Jupyter Notebooks, Tableau, R Studio, Scikit-learn, SQL databases.
Education BackgroundComputer Science or related field.Statistics, Mathematics, Computer Science, or Data Science.
IndustriesTechnology, finance, healthcare, e-commerce, gaming.Technology, finance, healthcare, research, marketing.

Conclusion

Choosing between a job in data science or software engineering depends on your interests, skills, and career goals.

Software engineers focus on building and maintaining systems, while data scientists specialize in analyzing and interpreting information to drive decision-making. Both roles are essential in today’s tech-driven world and offer promising opportunities.

By understanding the unique aspects of each profession, you can make an informed decision and embark on a rewarding career journey.

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