BayesMetric Analytics
Data science and analytics for business and finance — 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:-> Generalist data science for business
-> Financial data science and quantitative analytics for financial institutesClick on 'general' or 'financial' to learn more about these use cases
Use Cases
Price Optimisation and Dynamic Pricing
Companies know their product or service better than anyone else, but knowing how much to charge, is a tricky economics 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. Revenue increases of 2% lead to significant profitability drivers.
Demand Forecasting & Inventory Optimization
Predict demand accurately by analyzing historical sales patterns, seasonality, promotions, and external market influences. Reliable forecasting helps you minimize excess inventory, reduce stockouts, and optimize purchasing. Organizations implementing advanced demand forecasting commonly experience inventory cost reductions of up to 15%, translating directly into better cash flow and increased profit margins.
Flexible Data Science Resources
Staff consulting projects with on-demand data scientists who slot into your teams without full-time hires. Get expertise in areas like modeling, analytics, or MLOps exactly when you need it. Scale up or down with project demands, avoiding bench time and payroll overhead. Maintain consistent project quality and accelerate delivery with seasoned specialists. This approach boosts your win rate and client satisfaction while keeping costs predictable.
Churn Prediction
Analyze customer behavior, 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 personalized offers or proactive support. Catching at-risk customers early can cut churn by 10–30%. 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 logistics firms, retailers, industrials, healthcare providers and commodity trading houses turn raw data into clear decisions—whether that’s through forecasting, pricing optimisation, customer segmentation, etc.I work independently 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 analytics or AI can add value to your business. If it's something else I think you need more, 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.
Contact
Get in touch
Use Cases
Machine-Learning Signal Enhancement
Many desks already possess handcrafted signals that carry economic intuition but suffer from noisy execution. I can assist with layering scientific reasoning on top of your existing strategies, or using machine learning to select trades and optimise bet sizing can sharpen hit-rates without discarding institutional knowledge. Engagements focus on feature engineering, robust cross validation methods designed for trading and time series, and other data science methods in trading applications.
Model Validation and External Opinion
Founders and early investors often seek assurance of their technology teams' efforts before they show investors or their first large customers their products. Obtain an impartial, expert assessment of your predictive models before presenting them to investors or major clients. I conduct a comprehensive audit of your methodology, data integrity, performance metrics, and risk assumptions—then deliver a clear, formal report highlighting strengths, uncovering blind spots, and recommending actionable improvements. This assurance lets you proceed with confidence, demonstrating credibility and technical rigor to all stakeholders.
Back-Testing & Market-Simulation Frameworks
Backtest overfitting is one of algotithmic trading and quantitative finance's most persistent issues. A strategy performs well in testing and poorly in the real world. I can help your trading desk use validation techniques that will overhaul your approach to backtesting so you get reliable out of sample results.
Strategy Productionisation & Trade Automation
For clients with proven trading strategies—whether they’re spreadsheet-based, script-driven, or manually executed—I offer support to transition these into fully automated systems. This includes building robust, scheduled data ingestion processes, implementing trade execution logic using broker APIs such as Interactive Brokers or Alpaca, and wrapping everything in a clear, user-facing interface if needed. The goal is to create a reliable and transparent pipeline that operates according to your rules, reducing manual workload and enabling consistent, scalable deployment. This is suited to both individual quants and small teams seeking to professionalise their operations without losing visibility or control.