Finance Data Scientist ( Manager )

The practice

Finance is one of the most demanding and valuable environments in which to apply modern technology. You will work with complex enterprise data, mission‑critical processes and high‑impact decisions, using AI, data and engineering to reshape how organisations plan, control performance and allocate resources. The opportunity goes beyond building technically strong solutions: you will see how those solutions influence cash, profitability, risk and business growth, and take them from experimentation into trusted, production‑ready capabilities. Working in Finance Reinvention allows you to remain close to leading‑edge technology while developing an understanding of the CFO agenda, gaining exposure to senior decision‑makers and building the commercial judgement needed to solve enterprise‑wide challenges. This combination of deep technical capability, finance‑domain expertise and measurable business impact creates a differentiated career path that is difficult to develop in a purely technology‑focused role.

UKI Finance RP

Finance Data Scientist

The practice

Finance is one of the most demanding and valuable environments in which to apply modern technology. You will work with complex enterprise data, mission‑critical processes and high‑impact decisions, using AI, data and engineering to reshape how organisations plan, control performance and allocate resources. The opportunity goes beyond building technically strong solutions: you will see how those solutions influence cash, profitability, risk and business growth, and take them from experimentation into trusted, production‑ready capabilities. Working in Finance Reinvention allows you to remain close to leading‑edge technology while developing an understanding of the CFO agenda, gaining exposure to senior decision‑makers and building the commercial judgement needed to solve enterprise‑wide challenges. This combination of deep technical capability, finance‑domain expertise and measurable business impact creates a differentiated career path that is difficult to develop in a purely technology‑focused role.

Purpose of the role

Advances the Decision Intelligence capability from driver‑based planning and package configuration towards ML‑driven forecasting. Owns the forecasting models from problem framing and data preparation through model validation, production integration, monitoring and adoption. The role works alongside the Planning & Performance Management practice and extends the range of propositions the practice is able to take to market, combining statistical rigour with finance‑process understanding and decision‑ready explanation.

Responsibilities

• Build forecasting models on client financial and operational data, covering revenue, cost, cash, demand signals and underlying drivers, using appropriate classical, econometric, machine-learning or deep‑learning approaches rather than a single preferred method.
• Prepare and validate multi‑source data, engineer internal and external drivers, address seasonality and structural change, and model hierarchical relationships across products, entities, geographies or cost centres.
• Design rigorous back-testing and time-series cross-validation, compare against transparent benchmarks, and evaluate accuracy, bias, stability, calibration and business impact; reconcile forecasts across hierarchies where required.
• Run scenario and sensitivity analysis to a standard that supports CFO‑level interrogation, including stress cases, uncertainty ranges, forecast interventions and causal or counterfactual analysis where appropriate.
• Produce variance explanation and commentary capable of withstanding challenge from an FP&A team, including plan-versus-actual decomposition, driver attribution, explainability, confidence and limitations.
• Integrate models into the client planning cycle and EPM platform to support operational adoption, working with Data and ML Engineers on pipelines, APIs, model registry, versioning, deployment, monitoring, drift detection, retraining and controlled override workflows.
• Work with AI Engineers where forecasting intersects agentic workflow, including proactive variance alerting, hypothesis ranking and draft narrative generation, while retaining appropriate finance review and approval.
• Document methods, data, assumptions, model limitations and validation evidence, and measure whether the solution improves decision quality, planning efficiency or forecast performance in use.

Essential Experience

• Depth in time series and forecasting methods, spanning classical and modern approaches, with the judgement to select appropriately, including seasonality, external regressors, rolling horizons and model trade‑offs.
• Python and the associated analytical stack, with experience of production or near-production deployment, together with SQL, Git, testing and reproducible analytical or ML pipelines.
• Strong understanding of forecast evaluation, including time-series cross-validation, back-testing, benchmark selection, error and bias metrics, uncertainty and stability over time.
• Experience working with large, multi‑source datasets and implementing input, output and consistency checks that address data‑quality risk in forecasting pipelines.
• Ability to explain model behaviour to a finance audience and defend underlying assumptions, uncertainty, limitations and the practical implications for decisions.
• Ability to work with FP&A, operational and technical stakeholders to align models with planning calendars, business assumptions, adoption workflows and measurable outcomes.
• At least 6 years’ relevant professional experience

Desirable

• FP&A or financial planning domain knowledge, including driver‑based planning, rolling forecasts, management reporting or variance analysis.
• Causal inference or uncertainty quantification, Bayesian or probabilistic forecasting, hierarchical reconciliation, optimisation or simulation.
• MLOps or cloud data‑science experience using platforms such as Databricks, Snowflake, Azure ML, SageMaker or Vertex AI.
• Experience with Anaplan, OneStream, Pigment, Oracle EPM or SAP Analytics Cloud, including integration of external models or analytical services.
• Experience with anomaly detection, automated narrative generation or agentic workflows for forecast monitoring and intervention.

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