What a Quantitative Analyst does on a data science team.

Skills and scope are agreed before delivery starts, so the first sprint is productive rather than a ramp-up month. The work runs to the same backlog, the same repositories and the same definition of done as your own team's.

Quantitative Analyst skills and technology we assign for

  • Building pricing, risk and forecasting models with documented assumptions.

  • Backtesting and sensitivity analysis, so a model's limits are known before it is trusted.

  • Producing the documentation that model validation and audit will ask for.

  • Stress testing models against historical scenarios and documenting where they break down.

  • Maintaining model documentation to the standard your validation function expects.

Seniority levels we assign.

Senior. Model risk in this field is commercial risk, and it needs experience. Seniority is one of the inputs in how the monthly fee is built.

When a Quantitative Analyst is the right assignment.

Specialist assignment, most common in financial services and insurance engagements. On an ongoing roadmap this role is most often assigned alongside a Data Scientist or a Data Analyst. All 4 roles in this service line can sit on the same agreement, and capacity moves between them at the monthly cycle rather than requiring a new contract. It is one of the assignments that make up data science at Azendo.

160 h
Typical monthly capacity for this role
Fixed
Monthly price, unaffected by leave or holidays
4–6 weeks
From signed scope to delivery starting
Monthly
Cycle to raise or lower committed hours

Questions about assigning a Quantitative Analyst.

Can your models pass model validation?

They are built with documentation, assumptions and backtesting evidence for that purpose. We will ask about your validation standard at scoping.

Which sectors do you work in?

Mostly financial services, insurance and logistics, where pricing and risk models carry real exposure.

Assign a Quantitative Analyst to your roadmap.

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