Forecast demand and plan resources
Estimate future demand using sales history, promotions, seasonality, and other relevant signals. Understand uncertainty so your team can weigh stock, capacity, and service decisions.
Data science, forecasting & machine learning consulting
Turn your data into decisions your team can use.
When demand is uncertain, customer behaviour is hard to explain, or useful data is scattered across systems, the first step is a clear business question. I help turn that question into analysis, a forecasting model, or a data product that fits how your team works.
Discuss your business challengeWork directly with Sirajuddin Mohammed · Remote consulting worldwide
Where I can help
Start with the work you want to improve.
Understand customers and performance
Use segmentation, churn analysis, and predictive modelling to identify patterns and inform the next action. Connect findings to a decision rather than leaving them in an isolated report.
Bring a model into everyday operations
Turn a useful prototype into a maintained data product. Define data inputs, interfaces, refresh schedules, and monitoring around the people or systems that will use its output.
What an engagement can include
Agree on useful, testable deliverables.
- A review of the decision, available data, quality issues, and practical constraints.
- An agreed baseline and evaluation plan, including historical backtesting for forecasting.
- Analysis, data pipelines, or a model with documented assumptions and limitations.
- A dashboard, report, API, or integration appropriate to the agreed scope.
- Handover documentation and an agreed approach to monitoring and support.
How we assess whether it works
For a forecast, we test on later periods the model has not seen, compare against a simple baseline, and check performance at the planning horizon that matters. We also examine bias and uncertainty. For other models, evaluation follows the decision, the available labels, and the cost of mistakes. Business impact needs to be measured in use; model accuracy alone is not a commercial result.
What to bring to the first conversation
Bring the decision you want to improve, examples of your current reports or process, the data sources you can access, and any deadline. A short description is enough for the first conversation. We can begin with a focused assessment if the data or scope is still unclear.
Relevant work
See the problem, approach, and deliverable.
Practical reading for your team
- How to evaluate an ML model before putting it into use
- A practical demand forecasting plan for retail teams
Before we start
Can we start if our data is messy?
Yes. A data review can establish what is usable, what is missing, and whether a model is appropriate. Data cleaning or a reporting improvement may be the first useful deliverable.
Can you work with an existing analytics team?
Yes. I can take on a focused analysis, model build, evaluation, or integration alongside your team. Responsibilities, access, documentation, and handover are agreed at the start.
Do you work with businesses outside Australia and India?
Yes. I work remotely with teams worldwide. We agree on meeting overlap, data-access arrangements, milestones, and handover before starting.
Tell me what you want to improve.
Start with an introductory call to establish fit. If the opportunity needs investigation, we can scope a paid AI & Data Opportunity Assessment. With clear requirements, we can discuss a pilot or implementation directly.
Discuss your business challengeLooking for AI agents and automation?
The two services can be delivered separately or combined around your business need.
Agentic AI development & workflow automation ↗