Data and AI talent
Data and AI teams need very different people at each stage, from the analyst who answers this quarter's question to the engineer who keeps a model running in production. We recruit for every one of those roles.
How we approach it
Titles in data are loose. One company's data scientist builds dashboards; another's ships recommendation models. We start by asking what the person will actually produce in their first six months, then screen for that: the SQL and modelling for an analyst, the pipelines, orchestration and data volumes for an engineer, the experimentation and statistics for a scientist, and the deployment, monitoring and retraining for an MLOps engineer.
Gen AI roles move fastest. For LLM and agent engineers we look for people who have taken something past the demo stage: retrieval pipelines with real documents, evaluation suites, guardrails, cost control and production monitoring.
How it works
Define the output
What the person will build or answer, the stack they will use and who consumes their work.
Search
Targeted outreach to people doing this work now, alongside our existing data and AI network.
Hands-on screening
Technical conversations about pipelines, models and systems the candidate has built, and how they measured whether they worked.
Place and support
Short list, interviews, onboarding and an account manager for the length of the assignment.
Roles we place
Analytics
- Data analysts
- BI developers (Power BI, Tableau, Looker)
- Analytics engineers (dbt)
- Product and marketing analysts
Data engineering
- Data engineers (Spark, Kafka, Airflow)
- Snowflake and Databricks engineers
- Cloud data engineers on AWS, Azure and GCP
- ETL and integration developers
- Data architects and modellers
Data science and ML
- Data scientists
- Machine learning engineers
- MLOps engineers
- Computer vision and NLP specialists
- Decision science and forecasting
Gen AI
- Gen AI and LLM engineers
- AI agent and RAG developers
- Prompt and evaluation engineers
- AI platform engineers
- AI product managers
Data management
- Data governance and quality leads
- Master data specialists
- Data stewards
- Privacy and data protection analysts
What you can expect from us
- Screening on real work
- We ask about data volumes, failure modes, model monitoring and what went wrong in production, because that is where experience shows.
- Current with the market
- The tools change every year. Our recruiters track which skills are actually in demand and what they cost, and we share that with you.
Questions
Can you find people with both data and SAP experience?
Yes. Because we also run an SAP practice, we can find data engineers and analysts who understand SAP data models, extractors, Datasphere and Business Data Cloud, which is a rare combination.
Do you place Gen AI engineers on contract?
Yes. Many clients bring in Gen AI engineers on contract to build a first production use case, then hire permanently once the direction is clear.
Ready to talk about the role?
Tell us what you need. We'll reply with questions or a plan, usually within one business day.