Data Science, delivered by vetted seniors
24h
first response
72h
from brief to shortlist
1 in 7
clears our vetting
75/100
the pass mark
Mahala places senior Data Scientists into enterprise and government teams building models that change decisions. Every profile scored against our vetting protocol before it reaches your shortlist.
What a wrong data science hire costs
A wrong Data Science hire produces confident answers that are subtly wrong: correlation dressed as causation, a forecast that fits history and misses the turn, an uplift model that targets customers who would have converted anyway. The model runs, the dashboard renders, and the business makes worse decisions with more conviction than before.
That is why we score Data Scientists on decision impact, not model inventory. Anyone can fit a gradient boosting model. The question is whether their work changed what the business did and whether they can defend the causal claim behind it to an audit team.
What our Data Scientists deliver
01
Risk and fraud modelling for financial services and insurance.
02
Forecasting, optimisation, and causal inference, in production, not in a deck.
03
Translation of ambiguous business questions into testable analytical hypotheses.
04
The engineering literacy to move analytical work from notebook to production and keep it there.
When teams
choose Mahala
Every Data Scientist on the bench has cleared our vetting protocol: scored on evidence across technical depth, demonstrated impact, consulting aptitude, and trajectory.
In this discipline the consulting score carries real weight: scoping, stakeholder rigor, and the honesty to say what the data can't support are what separate senior from mid-level. 75/100 is the gate. Six out of seven don't make it.
How an
engagement runs
- Brief us: role, stack, project phase, timeline, access model (VDI).
- We talk it through: a short call to sharpen the brief.
- Two or three blind CVs within 72 hours of a clear brief.
- Interview the finalists, choose the best fit.
- Mahala handles contracting, screening where required, and onboarding. One contract, one monthly invoice.
A Representative
profile
Senior Data Scientist, PhD-trained, peer-reviewed at a top-tier machine learning conference on causal inference methods. Built the end-to-end causal uplift pipeline behind a marketing-technology product: modelling, experimentation, CI/CD, Airflow, MLflow, delivering a 10 to 15% lift in client campaign conversion. Prior production work spans energy R&D forecasting and geospatial analytics, including cutting a core pipeline runtime from 25 hours to 8. Vetted at 94/100.
Related
questions
What kind of Data Science work do your specialists deliver?
Risk and fraud modelling, forecasting, optimisation, causal inference, and the translation of business questions into testable analytical hypotheses. Models that change decisions, not slides.
How do you vet Data Scientists?
Two layers of rigorous, evidence-based assessment. Structured technical assessment first, then a consulting-fit interview covering scoping, stakeholder rigor, and audit-readiness.
Can your Data Scientists operate in regulated environments?
Yes. Most of our placements involve explainability, validation, and governance requirements. Operational discipline in regulated environments is part of the vetting bar.
How do you handle the notebook-to-production gap?
Every Data Scientist in our network has the engineering literacy to ship models into production decision pipelines, not just into notebooks the engineering team will not trust.
What's the typical engagement length?
Most engagements run 3 to 12 months, with extended retainers for multi-year analytics programmes.