BayesMetric Analytics

Data science and quantitative analytics for business and trading - from quick insight projects to full machine learning systems.BayesMetric Analytics helps small to mid-sized firms make better decisions with their data across two core areas:

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Use Cases - General Business Analytics and AI

Many businesses I work with have useful data that they've never monetised. Questions ranging from how much to charge for their products to inventory and logistics can all be enhanced by harnessing this data and unlocking its value.

Price Optimisation and Dynamic Pricing

Companies know their product or service better than anyone else, but knowing how much to charge is a tricky economic question. Shifting from a static pricing mechanism to a data-driven, potentially dynamic mechanism allows companies to capture significant additional recurring revenue without additional expenditure on ambiguous marketing campaigns.

Demand Forecasting & Inventory Optimisation

Predict demand accurately by analysing historical sales patterns, seasonality, promotions, and external market influences. Reliable forecasting helps you minimise excess inventory, reduce stockouts, and optimise purchasing.

Flexible Data Science Resources

Many companies I work with, especially tech consulting companies, simply benefit from flexible data science resources with which to staff their projects. As an experienced Senior Data Scientist, I can integrate with your team's existing workflow quickly and seamlessly for short to medium term engagements.

Churn Prediction

Analyse customer behaviour, transaction history, and engagement metrics to spot patterns that precede cancellations. Build a predictive model that scores each customer by churn risk. Use those scores to trigger targeted retention actions - like personalised offers or proactive support. Lower churn means you spend less on acquiring new customers and protect recurring revenue.

About

I’m Nelson, a data scientist and quantitative analyst with experience delivering analytics, machine learning, and AI solutions across both commercial business settings and financial markets. In recent years, I’ve helped small hedge funds, CTAs, commodity and crypto trading firms, logistics firms, retailers, industrials and healthcare providers turn raw data into clear decisions.I work on short to medium-term projects, and I’m very laid-back about how engagements begin. Some clients come to me with a specific idea or brief; others just want to talk through what’s possible with their data. Either way, I’m happy to have a chat to explore whether quant analytics or AI can add value to your business. If I think it's a different tech skill you need, I'll refer you elsewhere.If you’re not quite sure what you need—that’s often the best place to start. Reach out to me in the contact section.

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Use Cases - Trading Data Science and Quant Analytics

Data science and quantitative analytics for systematic and discretionary trading operations - with particular experience in equities, commodities, and digital assets.Engagements typically fall into five areas:

Signal Research & Quantitative Development

Trading desks and portfolio managers often have well-developed market intuitions - patterns they've observed, relationships they believe exist, or discretionary rules that seem to work. What's frequently missing is a rigorous framework for testing whether these intuitions hold up statistically, under what regimes they perform, and how to size positions accordingly.This is where quantitative methods add value: formalising hypotheses into testable signals, engineering predictive features from market data, and applying statistical techniques that distinguish genuine edge from noise. Engagements typically involve bringing scientific discipline to insights that already exist within the firm.

Backtesting & Market-Simulation Frameworks

Strategies that backtest well but fail in production usually share common methodological problems - lookahead bias, overfitting, or unrealistic execution assumptions.Many capable trading desks have ideas worth pursuing that never make it to production - not because the ideas lack merit, but because the testing methodology doesn't give reliable answers. Validation engagements focus on fixing this: identifying issues in existing pipelines and implementing frameworks that produce honest out-of-sample estimates.

Strategy Productionisation & Trade Automation

Many trading operations execute manually, and for some strategies this makes sense - discretionary timing, small scale, or markets where full automation isn't practical. But for strategies with well-defined rules, automation offers clear advantages: execution without emotional interference, consistent application of sizing and risk limits, and freeing up time that would otherwise go to routine order management.Productionisation engagements cover the infrastructure required to move from manual or semi-manual execution to reliable automated operation: scheduled data ingestion, execution logic against broker and exchange APIs, position reconciliation, and the monitoring, alerting, and risk controls that allow a system to run without constant supervision.Suited to small funds, prop desks, and individual managers looking to reduce operational burden without sacrificing visibility or control.

Alternative Data Research and Signal Testing

Some trading strategies are driven by information that sits outside standard price and volume data.
This can include news, filings, web data, images, or other alternative datasets, provided the research is validated carefully.
Engagements typically focus on building features from the data, testing for stability across regimes, and checking that results survive realistic out-of-sample validation.