Data and AI roles
A reporting need, difficulty accessing data and a model to put into production do not call for the same hire. The Data and AI family brings together the roles that make this work possible and useful to teams.
Connect data with a concrete use
The Data Engineer builds the systems that collect, store and make data available. The Data Scientist uses and interprets it to answer a question or inform a decision. When models are used in production, work on them continues through deployment and monitoring their operation.
These responsibilities need to connect with the people who will use the results. Before defining a role, state the business question, identify the accessible data and clarify the decision or service expected. Agree with Engineering on responsibility for the production system and with the teams using it on how to assess its usefulness.
Understanding the different roles
Make data accessible and usable
Data engineering organises data collection, storage and availability. To distinguish your need for architecture, engineering or analytical transformation, describe the data to connect, its users and the quality problems to address. Job titles then help explore this remit in more detail.
- Data engineer
- Data architect
- Analytics engineer
Use data to inform a question
The Data Analyst produces analyses and dashboards, among other outputs, to make data useful for decision-making. The Data Scientist also develops machine learning models to address problems. The boundary is not rigid: an analyst may build models. Clarify the place of analysis, presenting findings and model development in the role.
- Data analyst
- Data scientist
Move from experimentation to monitored operation
MLOps practices connect testing, deployment, automation and model monitoring, among other activities. They help distinguish experimental work from operational responsibilities. A project may involve several specialists: specify who works on the model, its integration and its monitoring.
- ML engineer
- MLOps engineer
- LLMOps engineer
- AI engineer
Define specific AI mandates
For a need involving research, client integration or Data and AI leadership, describe the deliverables and the scope of decisions. Emerging titles need the same clarity: identify the system involved, its users and the evaluation criteria before choosing a job title.
- Chief Data / AI Officer
- Forward deployed engineer
- Prompt engineer
- AI researcher
Job profiles in this family
- AI engineer (artificial intelligence engineer)An AI engineer designs, integrates and evaluates artificial intelligence features for a company's products and users.
- Data EngineerBuilds and operates the pipelines that collect, move and expose data at scale: ingestion, warehouse, orchestration.
- MLOps EngineerThe MLOps Engineer automates the deployment of machine learning models to production and organises monitoring for the teams that operate them.
- LLMOps EngineerAn LLMOps Engineer maintains applications that use language models in production and monitors their quality, reliability and costs.
- AI reliability engineerThe AI reliability engineer helps keep AI model services reliable in production, from the user request to the infrastructure running inference.
- Chief Data & AI OfficerThe Chief Data & AI Officer leads data strategy and AI use to connect projects with business priorities.
- Data analystA Data analyst prepares and analyses data to help business teams understand their activity and make informed decisions.
- Analytics EngineerAn Analytics Engineer transforms data into reliable, tested and documented models for analysts and business teams.
- Data ArchitectThe Data Architect designs data models, flows and storage to meet business needs and guide technical teams.
- Data scientistA data scientist analyses data and builds statistical models to help business teams answer their questions and make decisions.
- ML Engineer (Machine Learning Engineer)An ML Engineer designs, trains and integrates machine learning models into software used by customers or teams within the company.
- Forward Deployed EngineerA Forward Deployed Engineer designs and deploys software solutions for a client, adapting them to the client's data, systems and ways of working.
- Prompt EngineerThe Prompt Engineer designs and evaluates the instructions given to language models to adapt their responses to the needs of business teams.
- Artificial intelligence researcher (AI Researcher)An artificial intelligence researcher designs and evaluates new methods to address an organisation’s scientific questions.
- Safeguards enforcement analystThey examine reported uses of an AI system, apply its usage policies and refer sensitive cases to the appropriate decision-makers.
Choose according to the problem you face
Two teams use conflicting figures
If a metric changes depending on its origin, examine definitions, transformations and sources before requesting a new dashboard. Explicitly assign responsibility for resolving these discrepancies and validating the resolution with users.
Data is difficult to retrieve
If analysis depends on manual extracts or unavailable data, describe the access and flows that need to be built. This need may point towards data engineering rather than a role mainly responsible for interpretation.
A model works in a demonstration
Before arranging its real-world use, define the deployment conditions, tests and monitoring expected. For a predictive model, monitoring its performance may lead to another iteration. Assign this responsibility rather than treating the demonstration as the end of the project.
An AI feature needs to become part of a product
If the need concerns a service used by clients, clarify the interactions with existing software and the failure scenarios to handle. Involve product and technical leads in defining the role and evaluating the result.
Define the resources as well as the outcome
Check the data available
List accessible sources and known restrictions. If preparing them is part of the role, make this work visible; otherwise, identify the team responsible.
Define evaluation around use
Clarify how users will examine the results. A technical metric can help monitor a model, but the brief must also describe the service expected and the consequences of an error.
Assign responsibility for follow-up after delivery
Name those responsible for the data, the system and further changes. Plan how feedback from use will trigger a correction, further analysis or another experiment.
Frequently asked questions
How do you scope a Data assignment when the business question is still unclear?
Plan an initial scoping stage with an identified user. Ask them to state the decision they are trying to make, the information they lack and a deliverable that can test the usefulness of the work. Limit this initial scope before extending the assignment to other uses.
How do you compare two candidates with different AI job titles?
Compare the responsibilities they held on actual systems: data preparation, experimentation, integration and operations. Ask about the context in which the candidate worked and what they personally took responsibility for, then compare this experience with the remit of the role you need to fill.
Sources
- Apec : Data Engineer
- Onisep : Data scientist (expert en mégadonnées)
- Google Cloud : MLOps: Continuous delivery and automation pipelines in machine learning
- IBM : What is MLOps?
- Apec : Data analyst
- Apec : Data scientist