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.
We specialise in two core areas:
- Generalist data science for business
- Financial machine learning for trading and other financial markets applicationsClick on 'use cases' to learn more about what we can do for your business
Use Cases
Manufacturing and Industrial
Manufacturing businesses may derive significant value from analyzing plant-level data to identify sources of inefficiency across production lines. Opportunities might exist to optimize shift allocations, machine scheduling, or material flow—particularly where downtime, waste, or inconsistent throughput are impacting output.
Efficency increases of 1% can make significant bottom line contributions.
Logistics & Supply Chain
Operators in logistics and warehousing could benefit from a deeper analysis of routing, fuel use, fleet deployment, or fulfilment patterns. Businesses managing regular deliveries or last-mile operations may wish to explore whether demand forecasting or adaptive route planning could improve on-time performance and reduce operating costs.
Retail & E-commerce
Retailers with established customer bases may find value in segmenting users based on purchase behavior, frequency, or product preferences. Such insights could support differentiated pricing, targeted promotions, recommendation engines, or stock optimization strategies aligned to distinct customer profiles.
Financial Services
Lenders or credit providers operating without dedicated data science capabilities might consider developing tailored credit scoring models using their own application or payment data. Classifying counterparties or applicants into risk-based cohorts could support more accurate limit setting, pricing of credit, or early-warning systems for default risk.
Quantitative Finance and Algorithmic Trading
Many hedge funds and proprietary trading firms continue to explore or struggle with the integration of machine learning into their investment process. While direct price prediction often underperforms in live trading, machine learning techniques may be better applied to areas such as signal filtering, regime classification, trade selection, or dynamic portfolio allocation.
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
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