Expertise
The analytical depth your problem needs.
Analytics engineering, decision science and applied systems: connected capabilities for taking a difficult question from evidence to operational use.
01 / Can you trust the foundations?
Analytics Engineering & Measurement
Build analytical models and measurement around the decisions they need to support. Make definitions, lineage and reliability visible.
From evidence to use
- Source events
- Tested models
- Shared metrics
- Decision workflow
- Data models & reliability
- Modular transformations, versioned pipelines, data contracts, freshness checks and observability. Work with dbt, Airflow, ClickHouse, BigQuery or Postgres where they fit the environment.
- Measurement & shared metrics
- Event design, instrumentation, GA4/GTM governance, KPI definitions and semantic layers that connect source events to a consistent analytical view.
- BI & analytical enablement
- Decision-led dashboards, embedded analytics, performance tuning, ownership and training. Support useful workflows in ThoughtSpot, Power BI, Hex, Superset and related tools.
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02 / What does the evidence support?
Advanced Analytics & Decision Science
Select the method that fits the decision. Make assumptions, uncertainty and practical constraints explicit before turning a result into a recommendation.
From evidence to use
- Decision
- Hypothesis
- Analysis & uncertainty
- Recommendation
- Experiments & causal questions
- Hypotheses, counterfactuals, allocation, exposure and guardrails. Design experiments and repeatable readouts around an agreed decision rule.
- Forecasting & scenarios
- Forecasts with useful horizons, uncertainty and backtesting. Explore demand, capacity, revenue or operational scenarios against the decisions they inform.
- Behaviour, pricing & lifecycle
- Cohorts, segmentation, retention and pricing analysis. Join behavioural evidence with modelling and stakeholder interpretation.
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03 / Will it work in practice?
Applied ML, Risk & Decision Systems
Connect predictive logic to an operational workflow, with evaluation, threshold choices and clear ownership.
From evidence to use
- Signals
- Evaluation
- Scoring & review
- Monitoring
- Behavioural signals & features
- Feature engineering, label quality and behavioural detection for fraud, abuse and other predictive questions. Check what the data can actually support.
- Evaluation & threshold design
- Compare models with existing rules, examine precision/recall trade-offs and review capacity, and shadow-test before changing live decisions.
- Production & ownership
- Scoring workflows, monitoring, retraining criteria, rollback paths and runbooks. Use Python and existing data infrastructure without unnecessary system complexity.
Explore the technical detail
Put the next question to work.
Bring the question, the available evidence and the constraints. Speak directly with the practitioner who will do the work.