Key Insights on 7 Data Science Roles, Responsibilities and Skills

From personalized recommendations on streaming services to predictive text on our smartphones, data science is behind the scenes.

This rapidly growing field encompasses various activities, such as data collection, processing, sophisticated modeling, and insightful analysis.

Read on to discover the different data science roles and responsibilities and see if any spark your interest and could be a potential career path for you.

Explore 7 Key Data Science Roles: Responsibilities and Skills Required

When people think of data science professionals, they likely picture data scientists. However, that is just one role within the broader field of data science, which encompasses various roles essential for processing, analyzing, and deriving insights from data. For example, data analytics differs from data science because it's a more specialized area within data science.

Below, we'll explore several of them to provide you with a comprehensive view of the field.

Data Scientist

A data scientist leverages advanced statistical methods, machine learning algorithms, and technology to provide actionable intelligence that drives strategic business decisions. Key responsibilities include:

• Gathering and cleaning data and conducting exploratory data analysis.
• Developing models to forecast trends and behaviors using machine-learning techniques.
• Creating visual representations of data insights.
• Collaborating with marketing, finance, and operations teams to integrate data insights into business strategies.

Skills and Qualifications

To excel as a data scientist, professionals need the following skills:

• Proficiency in Python, R, or SAS for data analysis and model development.
• Experience with SQL for database querying.
• Understanding of statistical methods and their applications.

To become a data scientist, you typically need a bachelor's or master's degree in data science, computer science, statistics, mathematics, or a related field. Moreover, certifications such as Certified Analytics Professional (CAP), Open Certified Data Scientist (Open CDS), Microsoft Certified Azure Data Scientist Associate, or other relevant data science and machine learning options can also be beneficial.

Data Analyst

Data analysts examine datasets to identify trends and make informed business decisions. They play a significant role in transforming raw data into meaningful information. Some of their key responsibilities include:

• Gathering data from primary and secondary sources and interpreting it to identify trends.
• Applying statistical tools to analyze data and produce actionable insights.
• Designing data collection systems and strategies to optimize statistical efficiency and quality.
• Creating detailed reports and dashboards that summarize data insights for senior management.

Skills and Qualifications

To work as a data analyst, you need the following skills:

• Proficiency in Excel, SQL, and statistical software like SPSS or SAS.
• Experience with Tableau, Power BI, or similar tools for creating visual reports.
• Understanding of basic and advanced statistical techniques.

A bachelor's degree in mathematics, statistics, computer science, information management, or a related field is typically required. A master's degree is beneficial for advanced roles, whereas certification is not mandatory but can help further enhance career prospects.

Data Engineer

A data engineer designs, constructs, installs, and maintains large-scale processing systems and databases. Some of the top responsibilities for professionals in this role include:

• Creating data pipelines to automate data collection, transformation, and loading (ETL).
• Developing scalable and efficient data architecture to support analytics and reporting needs.
• Providing the necessary infrastructure and tools for data scientists and analysts.

Skills and Qualifications

To succeed as a data engineer, one needs to have a specific skill set, which includes:

• Strong knowledge of SQL and NoSQL databases.
• Experience with technologies like Hadoop, Spark, and Kafka.
• Proficiency in Python, Java, or Scala.
• Ability to troubleshoot and resolve data-related issues.

For this role, you need a bachelor's degree in computer science, information technology, engineering, or a related field. Advanced degrees, such as a master's in data engineering or a related discipline, can further enhance job prospects and expertise. Additionally, coursework in database management, data warehousing, and big data technologies is highly beneficial.

Data Architect

A data architect creates blueprints for data management systems to integrate, centralize, protect, and maintain data sources. Their day-to-day duties include:

• Creating the overall structure for data management, ensuring it aligns with business requirements.
• Planning and implementing strategies for effective data management and recovery.
• Aligning data architecture with the organization's strategic objectives.
• Working with IT and data teams to ensure the architecture supports their needs and capabilities.

Skills and Qualifications

Data architects need the following skills to fulfill their duties:

• Expertise in managing and designing relational and non-relational databases.
• Knowledge of cloud platforms like AWS, Azure, or Google Cloud.
• Proficiency with tools like Visio, erwin, or similar.

To become a data architect, you need a bachelor's degree in computer science, information technology, or a related field. Additionally, advanced degrees, such as a master's in data architecture or information systems, can significantly enhance career prospects and expertise.

Machine Learning Engineer

A machine learning engineer designs and implements machine learning models and algorithms that leverage large data sets. Their key responsibilities encompass the following:

• Creating and fine-tuning machine learning models for tasks such as classification, regression, and clustering and deploying them into production environments.
• Running experiments to test model performance, tuning hyperparameters, and selecting appropriate algorithms to maximize accuracy and efficiency.
• Working closely with data scientists to understand data requirements and construct data pipelines that support model training and evaluation.
• Enhancing model accuracy, reducing latency, and ensuring models can handle large-scale data in real-time applications.

Skills and Qualifications

When it comes to their skillset, this role requires:

• Proficiency in Python and C++ for developing and optimizing algorithms.
• Expertise in TensorFlow, PyTorch, Keras, or similar frameworks for model development.
• Strong understanding of data structures, algorithms, and their application in machine learning.

A bachelor's degree in computer science, statistics, mathematics, or a related field is typically required for this role. Advanced degrees, such as a master's or Ph.D. in machine learning or artificial intelligence, as well as relevant coursework in algorithms, data structures, statistics, and machine learning, can significantly enhance career prospects and provide deeper expertise.

Business Analyst

These professionals help intertwine IT and business by using data analytics in order to assess processes, determine requirements, and deliver data-driven recommendations. Some of their main duties include:

• Evaluating existing business processes to identify inefficiencies and suggest improvements.
• Working with stakeholders to gather detailed business requirements and documenting them.
• Using data analytics to provide insights that support strategic decision-making.
• Collaborating with various teams to implement recommended solutions, ensuring they meet business needs.

Skills and Qualifications

• Proficiency in Excel, SQL, and data visualization tools like Tableau and Power BI.
• Familiarity with BI tools and their applications in analyzing business performance.
• Skills in eliciting, documenting, and managing business requirements.

A bachelor's degree in business administration, information systems, or a related field is required for entry-level positions in this field. Moreover, while not mandatory, certifications such as Certified Business Analysis Professional (CBAP) or PMI Professional in Business Analysis (PMI-PBA) can enhance career prospects and credibility in the field.

Product Manager

A product manager oversees the development and delivery of products, ensuring they meet customer needs and business goals. Other responsibilities include:

• Analyzing market trends, customer needs, and competitor products to inform product strategy.
• Working with design, engineering, marketing, and sales teams to develop and launch products.
• Overseeing the entire product lifecycle, from initial concept through development, launch, and post-launch analysis.

Skills and Qualifications

A product manager needs to master the following skills:

• Understanding methodologies like Agile, Scrum, or Kanban for managing product development.
• Experience with tools like JIRA, Trello, or Asana for tracking project progress.
• Ability to lead and motivate cross-functional teams.

Typically, the educational requirements include a bachelor's degree in business administration, marketing, or a similar field. Advanced degrees or an MBA can be advantageous. Additionally, certifications like Certified Scrum Product Owner (CSPO) or Product Management Certification by Pragmatic Institute can further validate skills and knowledge in the field.

How to Become a Data Scientist

The exact path to becoming a data scientist can vary depending on the specific role you aim for within the data science domain. You might have to adjust your educational and professional journey according to the requirements and expectations of your targeted position.

Most data science positions typically require a bachelor's degree in data science, computer science, statistics, mathematics, or a related field. This foundational education equips you with the essential skills and knowledge. After graduation, seeking entry-level positions such as a data analyst or junior data scientist is beneficial. These roles offer practical experience and a deeper understanding of real-world data challenges, which are invaluable for your professional growth.

While not always necessary, a master's degree in data science, machine learning, artificial intelligence, or a related discipline can enhance your expertise and job prospects. Similarly, certifications can validate your skills and knowledge, making you more competitive in the job market. 

How Much Do Data Scientists Make?

Data science is a lucrative career. The median annual wage for data scientists is $108,020. Salaries in this field can vary widely based on experience, location, and industry. The lowest 10 percent of data scientists earn around $61,070, while the highest 10 percent can make over $184,090. This wide range reflects the diverse opportunities and career paths in data science.

Conclusion

Each role within data science plays a crucial part in transforming data into actionable insights. If you're considering a career in this exciting field, UND offers a range of undergraduate and graduate degree options tailored to equip you with the necessary skills and knowledge. As a founding member of the Midwest Big Data Innovation Hub, UND also provides fantastic networking opportunities through collaborative projects and events. So, join us at UND and be part of a community shaping the future of data science. Ready to turn data into discovery? Your journey starts here.

FAQs