Junior Data Analyst

This is an unusual entry-level analytics opening because it sits at the point where real commodity decisions are made. Instead of building dashboards that nobody reads, you will help sourcing teams at a global food business understand why prices moved and what might happen next, while learning to use AI tools responsibly along the way.

Why This Role Matters

Food sourcing decisions depend on timely, trustworthy market evidence. When beef, grain or dairy prices shift, a sourcing team needs to know whether the cause is seasonality, freight costs, exchange rates or a genuine supply change. Your work feeds directly into that conversation through a weekly market note and supporting data.

The team is not looking for someone who simply runs AI tools and pastes the output. It wants someone who can verify what the tool produced and explain it clearly.

Market Research With AI Support

A large part of the job is finding and summarising evidence from public sources such as USDA and Meat & Livestock Australia. Approved AI tools can help you locate and extract information faster, but the responsibility for accuracy stays with you.

In practice this means you will compare figures and claims against the original document, record the source and date for each data point, and flag cases where two sources disagree. Spotting conflicting evidence early is one of the most valuable habits you can build in this role.

In market research, a number without a source and a date is only an opinion.

Keeping Datasets Clean And Reliable

You will maintain price, production and trade datasets that other analysts depend on. The routine here is less glamorous than the analysis itself, but it decides whether the analysis can be trusted.

SQL Python Excel

AI-generated code is a starting point, not an answer. Run it, compare the output with a manual check on a small sample, and confirm that row counts and totals make sense before relying on it.

Writing The Weekly Market Note

Building Excel analyses, charts and dashboard updates is only half the task. The other half is explaining what changed and why it might have changed. Each week you will draft a short note linking a price or supply movement to possible drivers such as seasonality, freight or exchange rates.

State What Remains Uncertain

Good analysts are honest about the limits of their evidence. If the data points to a likely cause but cannot prove it, say so plainly. Sourcing teams trust notes that separate what is known from what is assumed.

Forecasting Experiments You’ll Support

Under guidance, you will reproduce a simple time series baseline, run an AutoML experiment, and compare both on a later holdout period. This is a practical introduction to forecasting without being expected to design models on day one.

flowchart LR
A[Prepare Data] --> B[Baseline Model]
B --> C[AutoML Run]
C --> D[Holdout Comparison]
D --> E[Log And Share Results]

Reproducibility And Data Handling

Every experiment must be logged with its data, settings and errors so a teammate can rerun your work and get the same result. Client data must only be handled through approved systems, which is a standard expectation in any professional analytics environment.

Who Fits This Role

0 – 2 Years Experience Accepted

The Work Sample That Matters

This posting asks for something many applicants overlook: a project or work sample showing how you used an AI tool for research, data cleaning or analysis. Academic and personal projects are explicitly welcome.

What recruiters are likely to look for in that sample:

1. The problem you were trying to solve

2. How the AI tool was used

3. How you checked its output

4. What you found and how you explained it

Verification is the part that separates strong submissions from weak ones. Show where the tool made a mistake and how you caught it.

Skills Worth Building Before You Apply

If you have time before applying, spend it on a small project using a public commodity or food price dataset. Clean it, chart it, and write a short note explaining one price movement. This single exercise covers most of what the role asks for.

Useful areas to revise include basic descriptive statistics, simple time series concepts such as trend and seasonality, Excel pivot tables and charting, and SQL joins and aggregations. Familiarity with Git and either Power BI or Tableau will help you stand out.

Resume Tips For This Application

Keep your resume focused on evidence. Instead of listing tools, describe what you did with them, for example cleaning a multi-source dataset, building a dashboard, or testing AI-generated queries against real data. Mention any food, agriculture or economics related work prominently, since it is a direct advantage here.

Keywords for Resume

Data Analysis • Market Research • Commodity Markets • Excel • SQL • Python • Time Series Forecasting • AutoML • Data Cleaning • Power BI • Tableau • Git • Statistics • Reporting • AI-Assisted Analysis • Documentation

Final Thoughts

This opening suits candidates who enjoy turning messy data into clear, defensible explanations. It offers early exposure to global supply chain analytics, responsible AI use and forecasting practice, which together make a strong foundation for a long-term analytics career.