Sponsored LinkedIn post
A dedicated post to introduce your brand to my audience.
Daniel Ojo
Data Analyst | Data Scientist | AI/ML Engineer | Software Engineering and Accounting Background | I ensure evidence-traceable, first-Rate Insights from messy data.
Open to collaborations
Companies pay Daniel to write a post about their product on LinkedIn.
This page is where they book it. Below: Daniel's audience, recent posts and price. Naano, the platform behind this page, handles the brief, your approval of the post and the payment.
€25per post, fixed price
or 3 posts for €63 · You approve the post before it goes live
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About Daniel Ojo
I turn messy, high-stakes data into decision-grade systems - evidence-traceable dashboards, predictive models, and automation engines people can actually trust. My foundation is software engineering, supported by a BSc in Accounting. I’ve built booking and ticketing platforms, fleet management tools, real-time GPS tracking systems, and payment integrations end-to-end at Primero and Interswitch. My accounting background gave me a strong foundation in financial reporting, reconciliation, controls, and understanding how transactions flow through business systems. After completing my Master’s in Data Science, I expanded into analytics and AI/ML, bringing the same engineering discipline to financial and operational data. What that looks like in practice: ▪ Executive analytics: Evidence-traceable dashboards modeled on the reporting standards of organizations such as JPMorgan Chase, Walmart, Amazon, Toyota, and British Airways/IAG - with KPIs tied back to their underlying source data. ▪ Forensic analysis: Traced messy financial records for Goshen Apartments & Suites to identify an embezzlement pattern disguised as miscellaneous expenses — starting with guest records and bank reconciliation rather than surveillance. ▪ Predictive systems: Built a credit-scoring engine and a purchase-intent model using Random Forest, achieving 79% recall and 0.725 PR-AUC on 12,000+ imbalanced records. ▪ Automation: Built reconciliation workflows and a cross-border logistics tracking system designed to reduce manual investigation and surface operational exceptions. ▪ Operational intelligence: Built systems that diagnose, explain, predict, and recommend interventions for operational bottlenecks. ▪ AI explainability: Developing an LLM layer that explains dashboard anomalies in plain English while citing the exact source cells behind each claim. The principle connecting all of this is simple: A useful system should not only produce an answer. It should make the answer traceable. I’m currently open to Data Analyst, Data Scientist, AI/ML Engineer, and Full-Stack Software Engineer opportunities where analytics, engineering, accounting, and AI intersect. If you’re building systems where the quality of the decision depends on the quality of the data behind it, let’s connect.
SoftwareAIProductivity
My LinkedIn audience
- Followers
- 1,602
- Avg impressions/post
- 412.6
- Top 50 exported posts · median 208.5
- Engagement
- 6.9%
LinkedIn export · Jul 29, 2026 – Oct 26, 2026 · Provided by the creator, not independently verified by Naano
Recent posts
All posts on LinkedInDaniel Ojo
My fraud model had 100% recall in testing. Then I found the transaction it couldn’t see. I shipped a real-time fraud detection API this week and discovered a blind spot: It misses the first transaction in a rapid fraud burst. Not because the model is broken. Because, in isolation, that first transaction looks completely normal. That was the uncomfortable part. Passing your tests doesn’t mean your model is perfect. It means you tested what you thought of. So I documented the flaw instead of hiding it. And I’m taking it further. The next iteration will focus on detecting the opening move - before the burst has enough history to reveal itself. Code and full breakdown in the comments 👇 repo: https://lnkd.in/dXUS7gbC What’s the strangest edge case you’ve ever found in a model? #MachineLearning #DataScience #Python
27 reactions · 8 commentsViewDaniel Ojo
It started with one number. ₦485,000. At first, it looked like just another figure in a transaction file. Then came the questions. Where did it come from? Does it exist in the ledger? Does the payment gateway agree? Is it a duplicate? Is the amount actually the same? What happens if the systems disagree? Suddenly, it wasn't just a number anymore. It was a data problem. Then an engineering problem. Then a software problem. And eventually, a question of whether AI could explain what happened without inventing an answer. That is what fascinates me about this field. One problem can take you from data analysis → data science → machine learning → software engineering. The boundaries between these disciplines become a lot less obvious when you are trying to solve something real. Sometimes the interesting part isn't the technology you use. It's how far one question can take you. https://lnkd.in/dpeEA5Ge
26 reactions · 2 commentsViewDaniel Ojo
What if an AI could explain financial data but wasn’t allowed to make anything up? That was the principle behind the LLM layer I built on top of my reconciliation engine. The rule is simple: If the data can’t prove it, the model can’t say it. Every number in the explanation must trace back to a field in the input JSON. No invented figures. No fabricated statuses. No made-up categories. The reconciliation engine remains the source of truth. The LLM comes second. It doesn’t decide what happened. It explains what the system can prove happened. The engineering behind it: → "context.py" builds a controlled text block from the JSON → "prompt.py" constrains the LLM to narrate only what the context supports → "client.py" calls Google Gemini and returns the explanation Working end-to-end with Gemini. Repo: https://lnkd.in/dppy9zR5 #Fintech #Python #LLM #BuildInPublic
5 reactions · 3 commentsView
Work with Daniel Ojo
Choose a format for your next LinkedIn collaboration.
€25
3 LinkedIn posts
A series of posts to build visibility over time.
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- Review the content before publication
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How does this price compare? B2B creator rates 2026
Who sees my posts
People who saw my posts, from my LinkedIn export.
People who saw my posts, from my LinkedIn export.
People who saw my posts, from my LinkedIn export.
People who saw my posts, from my LinkedIn export.
People who saw my posts, from my LinkedIn export.
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Booking Daniel Ojo: frequently asked questions
Daniel Ojo is a B2B LinkedIn creator based in Nigeria with 1,602 followers, posting about Software, AI, and Productivity. On Naano, one sponsored LinkedIn post by Daniel Ojo costs €25, a fixed price per post.
- How much does Daniel Ojo charge for a sponsored LinkedIn post?
- One sponsored LinkedIn post with Daniel Ojo costs €25 on Naano. It is a fixed price per post, the amount your brand pays. Packs of several posts are listed in the offers above. Compare with the B2B creator rates 2026 report
- How do I book Daniel Ojo?
- Create a brand account on Naano, choose an offer on this page and send your brief. The price is held when Daniel Ojo accepts and paid when the post URL is submitted. You can turn on draft approval to review the post before it goes live.
- Can Naano run the campaign for me?
- Yes. With Managed Campaigns, the Naano team sets the strategy, casts the creators, writes the briefs, reviews the drafts, pays the creators and reports results per creator and campaign. It is a three-month programme: a management fee from €3,000 per month, plus a creator budget from €6,000 per month. See Managed Campaigns
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