Behind the Data Curtain: Unveiling Key Roles in Data Science

In today’s data-driven world, “data science” often conjures images of complex algorithms, intricate models, and vast oceans of data. But who are the masterminds behind these cutting-edge technologies? What roles do they play in transforming raw data into actionable insights that drive businesses, innovate solutions, and shape our future?

Data Scientist — The Magician of Metrics

Data scientists use data to create insights and drive business decisions. They use various tools and techniques to collect, process, and analyze data, then present their findings to help organizations make strategic decisions.

Data scientists determine the questions their team should ask and figure out how to answer them using data. They often develop predictive models for theorizing and forecasting.

A data scientist might do the following tasks on a day-to-day basis:

• Find patterns and trends in datasets to uncover insights.
• Create algorithms and data models to forecast outcomes.
• Use machine learning techniques to improve the quality of data or product offerings.
• Communicate recommendations.
• Stay on top of innovations in the data science field.

Skills Required — Python/R, SQL, Machine Learning, Deep Learning, Statistics, Cloud Computing, Visualization tools, Big Data, Github, Model Deployment, etc.

Data Analyst — The Detective of Data

A data analyst is a professional who collects, analyzes, and interprets data to help organizations make decisions. They work with data from various sources, including company databases and external sources, to identify insights that can help businesses maximize the value of their data.

Generally speaking, though, the tasks data analysts must perform daily include the following:

• Designing and maintaining data systems and databases; this includes fixing coding errors and other data-related problems.
• Mining data from primary and secondary sources, then reorganizing the data in a format that can be easily read by either humans or machines.
• Using statistical tools to interpret datasets, paying particular attention to trends and patterns that could be valuable for diagnostic and predictive analytics efforts.
• Demonstrating the significance of their work in the context of local, national, and global trends that impact both their organization and industry.
• Preparing reports for executive leadership that effectively communicate trends, patterns, and predictions using relevant data.
• Collaborating with programmers, engineers, and organizational leaders to identify opportunities for process improvements, recommend system modifications, and develop policies for data governance.
• Creating appropriate documentation that allows stakeholders to understand the steps of the data analysis process and duplicate or replicate the analysis if necessary.

Skills Required — Data Visualization tools, Excel, SQL, Python/R, SAS, Statistics, etc.

Data Engineer — The Builder of Data Highways

A data engineer integrates, transforms, and consolidates data from various structured and unstructured data systems into structures that are suitable for building analytics solutions. The data engineer also helps design and support data pipelines and data stores that are high-performing, efficient, organized, and reliable, given a specific set of business requirements and constraints.

These are some common tasks you might perform when working with data:

• Acquire datasets that align with business needs
• Support the development of data streaming systems
• Implement new systems for data analytics and business intelligence operations
• Develop business intelligence reports for company advisors
• Develop algorithms to transform data into useful, actionable information
• Build, test, and maintain database pipeline architectures
• Collaborate with management to understand company objectives
• Create new data validation methods and data analysis tools
• Ensure compliance with data governance and security policies

Skills Required — Cloud Services, SQL/ No SQL, Big Data, etc

Business Analyst — The Interpreter of Business Needs

Business analysts use data to form insights and recommend business and other organizational changes. Business analysts identify areas that can be improved to increase efficiency and strengthen business processes. They often work closely with others throughout the organization’s hierarchy to communicate their findings and help implement changes.

Tasks and duties can include:

• Identifying and prioritizing the organization’s functional and technical needs and requirements.
• Using SQL and Excel to analyze large data sets.
• Compiling charts, tables, and other elements of data visualization.
• Creating financial models to support business decisions.
• Understanding business strategies, goals, and requirements.
• Planning enterprise architecture (the structure of a business).
• Forecasting, budgeting, and performing variance and financial analysis.

Skills Required — SQL, Python, Visualization tools, Excel, etc.

ML Engineer — The Engineer of Learning

A machine learning engineer (ML engineer) is a programmer who designs and builds software that can automate artificial intelligence and machine learning (AI/ML) models.

ML engineers build large-scale systems that take in massive data sets and use them to train algorithms that can learn cognitive tasks and generate useful insights and predictions.

Machine learning engineers manage the entire data science pipeline, including sourcing and preparing data, building and training models, and deploying models to production.

To achieve these and related tasks, machine learning engineers perform the following activities in an organization:

• Analyze big datasets and then determine the best method to prepare the data for analysis.
• Ingest source data into machine learning systems to enable machine learning training.
• Collaborate with other data scientists and build effective data pipelines.
• Build the infrastructure required to deploy a machine-learning model in production.
• Manage, maintain, scale, and improve machine learning models already running in production environments.
• Work with common ML algorithms and relevant software libraries.
• Optimize and tweak ML models according to how they behave in production.
• Communicate with relevant stakeholders and key users to understand business requirements, and also clearly explain the capabilities of the ML model.
• Deploy models to production, initially as a prototype, and then as an API that can serve predictions for end users.

Skills Required — Machine Learning, Deep Learning, Model Deployment, Python/R, Cloud Services, etc.