Data Science Hierarchy of Needs + Role Fulfillment

The journey from data to enlightenment is not solitary; it thrives within well-organized data science teams. However, it’s often the case that organizations aren’t ready to derive insights from data science, or implement AI.

Most commonly they are yet to build the infrastructure to implement even the most basic data science algorithms and operations.

We take a look at how to build this infrastructure, by working backwards and identifying the needs of a functional data science unit, and connecting them with the different roles that are filled to form a data science team.

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If we were to adopt a system similar to Maslow’s Hierarchy of Needs for the realm of Data Science and AI, with needs at the top of the pyramid requiring those below them to be fulfilled, we would arrive at something like this:

Implementing AI/Deep Learning is at the top of the pyramid, but first, the basic needs of data literacy, collection, infrastructure, etc. must be fulfilled:

Basic Needs

Data collection: What data do you need? What’s available? How are relevant user interactions logged? What data is coming through and how?

Data flow: How does data move through the system? Are your data streams reliable? How is it stored? How is it accessed?

Data exploration and transformation: How is your data cleaned? What processes are in place?

Analytics/Aggregations: What metrics are you tracking? What consideration are being made for data sensitivity and seasonality?

Experimentation: What frameworks are in place for incremental A/B testing and experimentation

All these layers are important in an organization working with data and require specialists with a specific skill set. The structure of data teams is tied to the process of working with data.

The data process initiates from the project manager, moving on to data engineers in charge of data collection and policy. It then progresses around the cycle to junior data scientists providing aid, proceeds to seasoned data scientists managing model creation, and ultimately shifts to ML engineers who collaboratively deploy and govern the end product alongside the data scientists. See image below:

Do note that there is often a degree of ambiguity in data science roles, with the responsibilities assigned to roles varying on a case by case basis:

Project Manager

The Project Manager bridges the gap between data science teams, stakeholders, and business goals, ensuring that projects are successfully planned, executed, and delivered.

Project Execution and Monitoring: They oversee the execution of data science project tasks, making sure they are completed on time and within scope. Monitoring project milestones, key performance indicators (KPIs), and metrics helps them ensure that the project is on track.

Communication with Stakeholders: Regular and clear communication with stakeholders, including non-technical individuals, is essential. Project managers update stakeholders on project progress, challenges, and results, translating technical details into understandable insights.

Alignment with Business Goals: They bridge the gap between data science and business by understanding the organization's goals and ensuring that data science projects provide actionable insights that drive value and impact.

Lead Data Scientist

Lead Data Scientists need a combination of technical expertise, leadership skills, and strategic thinking.

Team Management: Lead Data Scientists often supervise and mentor other data scientists. They help in recruiting, training, and developing team members. They may also assign tasks, review work, and provide feedback to ensure the team's growth and productivity.

Collaboration: They work closely with cross-functional teams, including engineers, analysts, domain experts, and business stakeholders, to understand business needs, translate them into data science tasks, and communicate results effectively.

Data Strategy: They help define and execute the organization's data strategy, including data collection, storage, preprocessing, and quality assurance. They might also provide guidance on data governance and best practices.

Data Engineer

Data engineers build data pipelines, transform raw data into usable formats, and ensure data quality and reliability. Data engineers collaborate closely with data scientists and analysts to provide them with the clean and organized data they require for analysis and modeling. In essence, data engineers lay the foundation for effective data utilization and insights within an organization.

Data Architecture: Data engineers design the overall architecture of data systems, including data storage, retrieval, and processing mechanisms. They choose appropriate data storage technologies such as databases, data warehouses, and data lakes.

Data Integration: They integrate data from various sources, which may include databases, APIs, third-party services, and more. This involves designing data pipelines to extract, transform, and load (ETL) data from source to destination systems.

Monitoring and Maintenance: After systems are set up, data engineers monitor data pipelines, identify and resolve issues, and ensure data continuity and reliability.

Data Scientist

Data Scientists use advanced analytical techniques to extract insights, patterns, and knowledge from data, and translating these insights into actionable solutions for business or research problems.

Data Analysis and Exploration: Data scientists analyze and explore complex datasets to identify trends, patterns, and anomalies. They use statistical methods and data visualization tools to gain a deeper understanding of the data.

Model Testing: Data scientists assess the performance of their models using appropriate metrics and techniques. They validate models on unseen data to ensure they generalize well and are not overfitting.

Feature Engineering: Data scientists engineer relevant features from raw data to enhance the performance of models. This requires domain expertise and creativity to extract meaningful information.

ML Engineer

The Machine Learning (ML) engineer designs, develops, and deploys machine learning models and systems to solve complex problems and deliver intelligent solutions.

Model Development: ML engineers work on fine-tuning machine learning models. This includes selecting appropriate algorithms, feature engineering, data preprocessing, and hyperparameter tuning to optimize model performance.

Feature Engineering: ML engineers identify relevant features from the data that can enhance the predictive power of the models. This involves domain expertise and creative thinking to extract meaningful information.

Deployment: Deploying machine learning models into production environments is a critical aspect of the role. ML engineers design scalable and efficient deployment pipelines, integrating models with web applications, APIs, or other systems for real-time predictions.

See you next time,
Mukundan

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