Senior Consultant in London

• Firm
• Gini
• Location
• London

Education

Postgraduate degree

Benefits

• Excellent
• Functional areas
• Data Science
• Finance
• Risk & Compliance
• Industry
• Banking
• Financial Services

Four to six years’ experience. Offered at Senior Consultant or Manager, by what you can run.

The firm

We are a UK consultancy working for regulated lenders, from long-established institutions to recent entrants. Three strands of work sit alongside each other here.

• Risk. The credit models behind lending decisions, the capital held against them, and the reviews that keep both defensible.
• Data. The engineering, pipelines and controls those models depend on.
• Applied AI. Where clients want to know what the technology is worth to them before committing to it.

The team is small, ambitious and growing quickly.

At this level you shape how we work.

The methodology you write becomes the way the firm does it, and the people you train become the bench behind you.

The appointment

This post sits between the technical work and the people doing it.

You will own the methodology on an engagement, run the workstream and the colleagues on it, and hold the client relationship between steering meetings.

Expect to lead an IFRS 9 rebuild for a specialist lender, take an IRB development through to supervisory submission, or validate somebody else’s model estate and defend the findings in the room.

The level is set at offer

Senior Consultant and Manager differ by how much you run without supervision, and by how much of the client sits with you. We will be direct about which we are offering and why.

Who we are looking for

• 4 to 6 years in credit risk modelling, model validation or a closely related quantitative role.
• Depth in at least two of IFRS 9, IRB, scorecard development and model validation, with the range to pick up the rest.
• Evidence you have owned a workstream end to end: the method, the timetable and the write-up, with a director reviewing rather than steering.
• Experience reviewing other people’s models and other people’s code, and giving feedback that lands.
• The judgement to choose a method and the written argument to defend it to a validation function or a supervisor.
• Comfort in front of a client: running the session, taking the challenge, and saying when something will be late.
• A strong degree in a quantitative discipline: mathematics, statistics, econometrics, physics, engineering, computer science or finance. An MSc is a plus but not required.
• Strong Python and working SQL. Version control as a habit.
• Methodology
• Choose the approach and own it, from segmentation through to testing.
• Set the standard the workstream codes and documents to.
• Decide what to do when the data will not support the method.
• Write the argument that defends the choice to a validator.
• Delivery
• Run a workstream to budget and timetable, with a director reviewing.
• Plan the resourcing, and say early when it will not hold.
• Keep the deliverable publishable at every milestone.
• Carry the work through to supervisory submission where it goes there.
• Client ownership
• Hold the relationship between steering meetings.
• Run the harder conversations: scope, slippage, findings they dislike.
• Learn the politics as well as the portfolio.
• Spot the next piece of work before they ask for it.
• Review and quality
• Review the team’s models and code before a client sees them.
• Give feedback specific enough to act on.
• Hold documentation to a standard the PRA could read unaided.
• Own the errors that get through.
• Leading people
• Set the work for consultants and juniors, then review the output.
• Train rather than correct. Explain why the method was chosen.
• Give an honest read on performance, early and in person.
• Commercial
• Shape proposals: approach, methodology, pricing logic, write-up.
• Scope a piece of work so that it can actually be delivered.
• Contribute to what the firm publishes.
• Agency
• Own the outcome. Work out what needs doing, do it, then tell us.
• Where nothing is written down, form a view and bring your reasoning.
• Spot the gap in the method, the data or the team, and close it.
• Tooling
• Use AI tools across modelling, documentation and review.
• Build prompts, skills and automations that compound across engagements.
• Set the standard for how the team uses them.
• The ground you will cover

Depth in some of it, working knowledge of the rest.

• Service lines Credit risk modelling; IFRS 9 and impairment; IRB and capital; scorecards and decisioning; model validation, monitoring and model risk.
• Regulation IFRS 9; Basel 3.1 and CRR; the PRA IRB rulebook; EBA Guidelines on PD and LGD estimation; SS1/23 model risk management principles; Consumer Duty where it touches affordability.
• Models and methods PD, LGD, EAD and ECL; application and behavioural scorecards; logistic regression, GLMs, survival analysis, time series; gradient boosting and other machine learning; calibration, segmentation, back-testing, benchmarking and monitoring.
• Portfolios Retail mortgages, unsecured lending, credit cards, motor finance, SME and specialist lending.
• Tools Python (primary); SQL; Git; Excel to a high standard; exposure to AWS, Azure or GCP is a plus; SAS or R is useful where you have it.
• Adjacent Stress testing and ICAAP; credit strategy and affordability; COREP, FINREP and Pillar 3; data quality and lineage.
• Wider firm Data engineering, pipelines and data governance; machine learning development and deployment; applied AI strategy, tooling and safety.
• Progression and culture

Where you land depends on the level we offer and on how much you can run without supervision. We review it as your scope grows.

We are a small firm, and this is a building job as much as a delivery one.

We look for people who are technically strong, intellectually honest, ambitious, and who show up for the culture.

The post is office-based, five days a week, because the firm is at the stage where the team learns the most from being in the same room.

• Job information
• Firm: Gini

Education: Postgraduate degree

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