The demand for data science professionals is skyrocketing, with the job market expected to grow by 21% over the next decade. If you’re pursuing a degree in data science, you might be wondering—what comes next?
The good news is that a degree in this field opens doors to multiple career paths, spanning various industries and skill levels.
At TheClickReader.com, we provide resources for aspiring data professionals, from structured courses to career guidance.
In this guide, we’ll break down the different roles you can pursue, their responsibilities, required skills, salary expectations, and strategies to land your first job.
A data science degree gives you access to multiple career paths, from entry-level positions to highly technical roles.
Below are some of the most common roles in the field, along with their responsibilities, required skills, salary expectations, and career progression.
A Junior Data Scientist works with raw data to uncover insights, applying statistical analysis and machine learning to support business decisions.
Responsibilities include data cleaning, trend identification, model building, and visualization. Proficiency in Python or R, machine learning algorithms, and data visualization tools is essential.
With an average salary of ~$100,265 annually, this entry-level role suits professionals with zero to two years of experience.
Internships and projects help build credibility. Career growth can lead to positions like Senior Data Scientist, Lead Data Scientist, or Data Science Manager.
A Data Analyst transforms structured data into insights that guide business decisions, focusing on reporting and trend analysis rather than machine learning.
Responsibilities include writing SQL queries, developing dashboards in Tableau or Power BI, and conducting statistical analysis. Essential skills include SQL, data visualization, and statistics.
With an average salary of ~$65,246 per year, entry-level roles exist, though some require one to three years of experience, especially in business intelligence.
A Junior Data Engineer focuses on building and maintaining data pipelines, ensuring efficient storage and processing of large datasets.
Responsibilities include managing ETL pipelines, databases and data warehouses, optimizing workflows, and ensuring data security.
Key skills include database management (MySQL, PostgreSQL), cloud computing (AWS, Azure, GCP), and ETL processes.
With salaries ranging from $90,000 to $160,000 per year, this entry-level role suits professionals with zero to two years of experience, with internships and projects providing an advantage.
A Machine Learning Engineer develops and deploys machine learning models for applications like recommendation systems, fraud detection, and NLP.
This role leans heavily on software development, requiring expertise in Python, R, or Java, deep learning frameworks (TensorFlow, PyTorch), and production-ready code.
Responsibilities include training, optimizing, and deploying models, often at scale using big data technologies.
With salaries ranging from $110,000 to $160,000 per year, this role typically requires two+ years of experience, with many transitioning from Data Scientist or Software Engineer positions.
A Data Architect designs and manages an organization’s data infrastructure, ensuring efficient and secure data flow across systems.
Responsibilities include creating database architectures, defining governance policies, overseeing security, and implementing scalable storage solutions.
Key skills include database management, ETL processes, cloud platforms, and distributed computing.
With salaries ranging from $100,000 to $180,000 per year, this senior role typically requires five+ years of experience in data engineering, database design, or system architecture.
Beyond traditional data science jobs, there are specialized paths that combine data science with other disciplines.
A Business Intelligence (BI) Analyst helps organizations make informed decisions by analyzing trends, patterns, and performance metrics.
This role bridges data science and business strategy, requiring both technical and analytical skills.
Responsibilities include developing dashboards (Tableau, Power BI), querying databases (SQL, Excel), and presenting insights to executives.
With two to four years of experience in data analysis or reporting, professionals in this role refine data reporting strategies to enhance business outcomes.
An AI/NLP Specialist develops applications that process and analyze human language, playing a key role in chatbots, speech recognition, and automated content analysis.
Responsibilities include building NLP models, developing recommendation systems, and fine-tuning large language models.
Key skills include deep learning frameworks (TensorFlow, PyTorch), NLP libraries (NLTK, spaCy, Hugging Face), and computational linguistics. This specialized role typically requires three+ years of experience in AI, deep learning, or NLP projects.
A Data Visualization Specialist transforms complex datasets into interactive dashboards and compelling visuals to help stakeholders interpret trends.
Responsibilities include designing reports, creating clear graphics, and improving data storytelling in collaboration with business teams. Key skills include Tableau, Power BI, Python visualization libraries (Matplotlib, Seaborn, Plotly), and UI/UX principles.
This role typically requires two+ years of experience in data visualization or business intelligence.
A Research Scientist applies data science techniques to tackle complex academic or industry challenges, focusing on machine learning, AI, and statistical modeling.
Responsibilities include developing new methodologies, publishing research, collaborating with institutions, and creating innovative algorithms. Key skills include statistics, machine learning, probability theory, and data modeling.
This role typically requires a Master’s or Ph.D. in data science, computer science, or a related field.
A Data Journalist blends storytelling and data science to uncover and report insights, making it a key role in newsrooms and investigative reporting.
Responsibilities include analyzing datasets, creating interactive visualizations, and writing data-driven articles. Key skills include data analysis, visualization, and clear communication of complex findings.
With an average salary of $41,431 per year, experience varies, but strong data storytelling and analytics expertise are essential.
A Market Research Analyst helps businesses understand consumer behavior, industry trends, and competition using data-driven techniques.
Responsibilities include analyzing buying patterns, conducting surveys and focus groups, and presenting insights to marketing teams.
Key skills include analytical and statistical expertise, survey analysis, and market intelligence tools.
With an average salary of $74,680 per year, entry-level roles are available, though experience in market research or data analysis is beneficial.
A Risk Management Analyst evaluates financial, operational, and regulatory risks to protect a company’s stability, commonly working in finance, insurance, and compliance.
Responsibilities include analyzing risk factors, developing mitigation strategies, and ensuring regulatory compliance. Key skills include risk assessment frameworks, financial modeling, and data analysis.
Experience in risk analysis, finance, or compliance is required, with specific qualifications varying by industry.
A Quantitative Analyst (Quant) uses mathematical and statistical techniques to analyze financial markets and investment strategies, primarily working in hedge funds, investment banks, and financial institutions.
Responsibilities include developing trading strategies, forecasting market trends, and assessing financial risk. Key skills include statistical modeling, probability theory, and financial mathematics, with proficiency in Python, R, and MATLAB.
This role typically requires a Master’s or Ph.D. in mathematics, finance, economics, or a related field.
Data science offers a variety of career paths, each requiring different skills and expertise. Below is a comparison of key roles, outlining their responsibilities, salary expectations, and essential skills.
| Role | Responsibilities | Average Salary (USD) | Skills Required |
| Junior Data Scientist | Analyzing datasets, building machine learning models, data visualization | ~$100,265 | Python/R, machine learning, data visualization |
| Data Analyst | Querying databases, creating dashboards, statistical analysis | ~$65,246 | SQL, Tableau/Power BI, statistics |
| Junior Data Engineer | Managing ETL pipelines, optimizing data workflows, database management | $90,000 - $160,000 | SQL, cloud computing, ETL tools |
| Machine Learning Engineer | Developing and deploying ML models, optimizing algorithms | $110,000 - $160,000 | Python, TensorFlow/PyTorch, software engineering |
| Data Architect | Designing data infrastructure, ensuring security and compliance | $100,000 - $180,000 | Database design, ETL, cloud platforms |
| Business Intelligence Analyst | Creating dashboards, analyzing business trends, reporting insights | Varies ($75,000+) | SQL, Tableau/Power BI, business strategy |
| AI/NLP Specialist | Developing NLP models, chatbot development, deep learning | Varies ($120,000+) | TensorFlow, NLP libraries, computational linguistics |
| Data Visualization Specialist | Creating interactive dashboards, translating data insights visually | Varies ($90,000+) | Tableau, Power BI, Python visualization libraries |
| Research Scientist | Conducting research, publishing findings, developing new ML methods | Varies (academic & industry roles) | Statistics, machine learning, probability theory |
| Data Journalist | Investigating and reporting using data, creating visual stories | ~$41,431 | Data analysis, storytelling, journalism |
| Market Research Analyst | Studying consumer behavior, assessing market trends | ~$74,680 | Statistical analysis, market intelligence tools |
| Risk Management Analyst | Identifying financial risks, ensuring compliance, risk mitigation | Varies ($90,000+) | Risk assessment, financial modeling, compliance |
| Quantitative Analyst | Analyzing market trends, developing trading strategies | Varies ($120,000+) | Statistical modeling, Python/R, financial mathematics |
Below are the key skill areas needed to build a strong career in data science.
A solid foundation in programming, statistical analysis, and data management is crucial for any data science role. Some essential technical skills include:
Beyond technical knowledge, data scientists must excel in:
Understanding industry-specific challenges can give professionals an edge. For example, in healthcare, knowledge of compliance regulations is valuable while in finance, expertise in risk modeling can be crucial.
With technology evolving rapidly, staying updated is essential. Earning certifications like AWS Certified Data Analytics or the Google Data Analytics Professional Certificate can enhance credibility and career opportunities.
Data science professionals are increasingly sought after, with industries expanding data teams in 2025 to enhance operations, customer experiences, and innovation.
Landing a data science job requires more than just technical skills—it’s about standing out in a competitive market. Here are key strategies to improve your chances:
Staying updated and connected is crucial for career growth in data science. Here are valuable resources to enhance skills and expand professional networks:
A data science degree unlocks multiple career paths, from entry-level roles to senior positions.
Whether you’re passionate about data analysis, machine learning, or business intelligence, there’s a career path that fits your interests.
Stay updated with new tools, build a strong portfolio, and keep learning to increase your chances of success in this growing field.
