Data Science Team Roles

tests to evaluate the effectiveness of different strategies or interventions of two variables

A/B testing

Creating and developing new machine learning algorithms or adapting existing algorithms to solve specific problems

Algorithm development

Duties included: Reporting, Forecasting, Dashboard creation, Data analysis, Data collection, Data governance

Business Intelligence Analyst

Using cloud computing platforms to deploy and scale machine learning systems

Cloud computing

Collaborating with data scientists, software developers, and business stakeholders to identify and solve machine learning problems that align with business goals and objectives

Collaboration

Presenting complex data and analysis clearly and understandably to stakeholders across different functions and levels of the organization

Communication

Continuously monitoring and evaluating the effectiveness of decision-making processes and recommending improvements to drive better outcomes

Continuous improvement

Involves professional development, continuously monitoring and evaluating the effectiveness of decision-making processes, and recommending improvements to drive better outcomes

Continuous improvement

Creating dashboards to provide real-time access to key performance indicators (KPIs), which helps business leaders identify areas within the business that may require attention

Dashboard creation

Duties included: Data pipeline development, Data Storage management, Data quality control, Data Security, Data architecture design, ETL (extract, transform, load), Data integration, Managing big data, Cloud computing, Collaboration

Data Engineer

designs, builds, and manages the infrastructure that supports data processing, storage, and retrieval

Data Engineer

After data is collected, analyzing the data to identify certain trends and possible patterns

Data analysis

Utilizing statistical methods and data visualization tools to analyze large datasets, identify trends, and compile insights to assist organizations when making business decisions

Data analysis

Designing and implementing data architecture that meets the organization's requirements for scalability, performance, and reliability

Data architecture design

Cleaning and preparing datasets for analysis by correcting data errors, removing duplicates, and reviewing accuracy; these processes are also known as data filtering, data integration, data classification, data munging, and data summarization

Data cleaning, preparation, and processing

Collecting and gathering large datasets, including databases, surveys, and other data sources from various platforms

Data collection

Responsibility for the collection of data from various sources, which can include third-party sources, external databases, and internal systems

Data collection

Ensuring the data is accurate and secure, which includes reviewing quality data standards, reviewing data security measures, and monitoring data usage

Data governance

data from various sources, formats, and systems to create a unified view of datasets

Data integration

Data pipeline developmentdesigning and developing data pipelines that move data from source systems to data storage systems, which includes tasks such as data ingestion, transformation, and loading

Data pipeline development

A step in data analysis and machine learning that involves transforming and preparing raw data to make it suitable for analysis

Data preprocessing

Ensuring the quality of data by identifying and correcting errors, inconsistencies, and missing values

Data quality control

Creating clear, concise reports and visuals that easily communicate findings to team members and key stakeholders

Data reporting and visualization

a professional who uses statistical, machine learning, and programming skills to analyze and interpret complex datasets and to develop and deploy predictive models and algorithms that can be used to solve business problems

Data scientist

Implementing data security policies and procedures to protect sensitive data from unauthorized access, theft, or corruption

Data security

the storage of data in databases, data lakes, and data warehouses, which includes tasks such as data partitioning, indexing, and replication.

Data storage management

Developing narratives and presentations that explain complex technical concepts and insights to nontechnical stakeholders clearly and concisely

Data storytelling

Collaborating with various team members and stakeholders to identify opportunities for improvement to make data-driven decisions; you could make a data-driven decision by analyzing the data on customer behavior and preferences to identify which products are most popular and why

Data-driven decision-making

Duties included: Statistical analysis, Modeling and simulation, Optimization, Risk analysis, Decision support, Communication, Strategic planning, Innovation, Continuous improvement

Decision Scientist

focuses on creating and applying mathematical and statistical models to optimize decision-making processes and outcomes

Decision Scientist

Providing decision-makers with the tools and insights necessary to make informed decisions

Decision support

this type of analytics focuses on summarizing and describing historical data to provide insights into past trends and patterns. Descriptive analytics helps to answer questions such as "What happened?"

Descriptive Analytics

Diagnostic analytics analyzes past data to identify the root causes of specific outcomes or events. Diagnostic analytics answers questions such as "Why did an event happen?" and "What caused it to happen?"

Diagnostic Analytics

Extract, transform, load

ETL

Designing and conducting experiments to test hypotheses and validate assumptions within the data

Experimentation

This type of analytics involves exploring and analyzing data to identify potential trends, patterns, and relationships. Exploratory analytics is often used when there is no clear objective or question to answer

Exploratory Analytics

Utilizing historical data to forecast future trends to identify potential risks and opportunities within organizations; additionally, BI analysts can utilize forecasting beyond the structure of their organization for various services and products

Forecasting

identifying new opportunities for growth and innovation through data-driven insights and analysis

Innovation

Duties included: Data preprocessing, Model Selection, Model training, Model evaluation, Model Deployment, Software engineering, Algorithm development, Performance optimization, Cloud computing, Collaboration

Machine Learning Engineer

specialize in developing, designing, and deploying artificial intelligence (AI) models to solve complex problems.

Machine Learning Engineer

models to automate decision-making processes, such as recommendation engines, fraud detection systems, or chatbots

Machine learning

handling large volumes of data that are too complex or too large to be processed using traditional methods

Managing big data

Deploying machine learning models into production environments and ensuring the models are scalable, reliable, and efficient

Model deployment

Evaluating machine learning models on test data to measure the model's accuracy, precision, recall, and other performance metrics

Model evaluation

Selecting the appropriate machine learning models for the specific problem and data, which includes tasks such as selecting classification or regression models, neural networks, decision trees, and other models

Model selection

Training machine learning models on data using various algorithms

Model training

Building models and simulations to understand how variables interact and how they are used to affect decisions within an organization

Modeling and simulation

mathematical methods used to find the optimal solutions to a problem and to identify the best course of action that maximizes benefits and minimizes risks within an organization

Optimization

Optimizing machine learning models and algorithms for faster computation, lower memory usage, and higher accuracy

Performance optimization

Using historical data to forecast future outcomes, predictive analytics helps to answer questions such as "What is likely to happen in the future?" and "How can we prepare for it?"

Predictive Analytics

Using algorithms (sets of detailed instructions that are used to solve specific problems or calculate specific operations) to assist with predicting trends and future outcomes based on historical data

Predictive analysis

This type of analytics recommends actions that can be taken to optimize or improve a situation. Prescriptive analytics considers data analysis and modeling to provide specific recommendations on what to do next. It answers questions such as "What should we do?" and "How can we improve the outcome?"

Prescriptive Analytics

Creating reports based on certain findings, which organizations use to make decisions

Reporting

Assessing risks associated with different decision options and developing strategies to mitigate those risks

Risk analysis

Developing software applications that integrate machine learning models into products or services

Software engineering

to create and apply mathematical models that optimize decision-making processes and outcomes, using statistical models to analyze data and identify patterns and relationships that inform decisions

Statistical analysis

Collaborating with key stakeholders to develop long-term strategic plans that align with organizational objectives or goals

Strategic planning

Business Intelligence Analyst

primarily uses data to help businesses navigate daily decisions