I know you're probably busy, so I'll get straight to it. You've landed on this page, likely from New York City, because you're looking for someone to help make sense of your data, or maybe even build a predictive model. It's easy to get lost in the hype, but what I do is pretty grounded. I help companies use machine learning and data science to solve specific problems – like predicting customer churn, optimizing pricing, or automating some really tedious manual data tasks. I'm not here to sell you a 'digital transformation journey'; I'm here to build you something that works, that you can understand, and that ultimately helps your bottom line.
I operate out of Florida, but I work with clients all over the U.S., including quite a few right here in New York City, New York. The beauty of modern data work is that it's largely remote, so whether you're in Manhattan, Brooklyn, or anywhere else, we can connect easily. My promise to you is transparency, clear communication, and a focus on delivering tangible results, not just reports or vague recommendations. I'm a solo shop, so you're always working directly with me, which means no junior consultants or shifting points of contact.
## What I actually do for New York City clients
Okay so, what does this actually look like for a New York City business? Typically, I'm brought in when there's a specific business problem that data *might* be able to solve, but the internal team either lacks the expertise, the time, or both. For example, I've helped clients build custom models to forecast sales more accurately, identify which marketing channels are actually driving revenue, or even automate the classification of incoming customer service tickets. It’s about taking raw, often messy data and turning it into something actionable.
Another common scenario is when a client has a proof-of-concept for an AI idea, but it's stuck. I come in, evaluate the existing work, and then help build out a more robust, production-ready version of that model or data pipeline. This isn't about setting up 'big data platforms' (unless you really need one, and then I'll be honest about it), it’s more about focused, practical application of machine learning to get a concrete result, often within a few weeks or months.
## How a New York City engagement usually goes
Here’s a rough outline of how I typically work with my New York City clients:
1. **Initial Chat & Problem Definition (1-2 weeks):** We'll have a couple of calls to really dig into what problem you're trying to solve. This isn't just about 'using AI'; it's about understanding your business, your data, and what a successful outcome looks like. I'll ask a lot of questions. I might even tell you if I don't think I can help, or if I don't think machine learning is the right fit. After this, I'll send over a project proposal with a clear scope, deliverables, and a fixed price. No surprises. 2. **Data Deep Dive & Discovery (2-4 weeks):** Once we agree on a plan, I'll need access to your data. This often involves secure data transfer or working within your existing cloud environment. I'll spend time cleaning, exploring, and understanding the nuances of your dataset. This phase is crucial because the quality of the data directly impacts the quality of any model we build. It’s kinda like getting your ingredients ready before you start cooking. 3. **Model Building & Iteration (4-8 weeks):** This is where the machine learning magic happens, or rather, the careful statistical work. I'll build and test different models, always keeping your specific business goal in mind. We'll have regular check-ins where I explain my progress in plain language, show you early results, and get your feedback. This isn't a black box; you'll understand what's happening every step of the way. 4. **Deployment & Handoff (2-4 weeks):** Once we have a model that performs well and meets your objectives, I'll work with your team (or set it up myself, if you prefer) to get it integrated into your existing systems. This could mean providing a prediction API, automating a report, or simply giving you the tools to run the model yourself. I’ll make sure your team understands how to use it, interpret its results, and maintain it going forward. My goal is to leave you with something useful and sustainable.
## What it costs, roughly
I know budget is always a consideration. Because every project is unique, I don't publish hourly rates. Instead, I work on a **fixed-fee project basis**. This means you know the total cost upfront, with no unexpected invoices. For a typical machine learning pilot project – say, building a custom predictive model from scratch – you're generally looking at somewhere in the mid-five figures. Simpler data analysis or automation tasks might be less, and more complex, multi-stage engagements will be more. The exact price depends entirely on the scope, complexity, and expected duration of the work. I'm happy to discuss your specific needs and provide a detailed quote after our initial chat.
## Who I'm usually NOT a fit for
To be upfront, I'm not for everyone. If you're a Fortune 500 company looking for a massive team of consultants for a year-long 'digital transformation' initiative, I'm probably not your guy. I'm a solo operator, not a big agency. I also generally don't do 'staff augmentation' where I just fill a seat on your team for months on end. My focus is on well-defined projects with clear deliverables. If you're looking for someone to build a fancy website or develop a mobile app, that's also outside my wheelhouse. I specialize specifically in machine learning, data science, and data automation. I'm also not the cheapest option out there, and I don't compete on price. I compete on delivering real value and results for your New York City business.
## Getting in touch
If any of this sounds like what you're looking for, and you're ready to explore how machine learning or data science could genuinely help your business here in New York City, New York, I'd love to chat. The easiest way to start is to book a 20-min call directly on my calendar. We can discuss your situation, see if there's a good fit, and explore next steps. No pressure, no sales pitch, just a straightforward conversation. I look forward to hearing from you!
FAQs — New York City
How do I know if AI is worth it for my New York City business?
The best way to figure this out is by identifying a specific problem or bottleneck in your business operations. If you have data related to that problem, chances are machine learning could offer insights or automation. We can explore this together during an initial call.
Do you work with companies outside New York City?
Absolutely. While I have many clients in New York City, my work is primarily remote, allowing me to collaborate effectively with businesses across the entire United States.
What does the first 2 weeks look like after we agree on a project?
In the first two weeks, we typically focus on secure data access and initial data exploration. I'll be working to understand your datasets, their quality, and how they relate to the project's objectives, setting the foundation for model building.
What kind of data do you need to get started?
I need relevant historical data that pertains to the problem we're trying to solve. This could be anything from sales figures and customer demographics to operational logs or sensor readings. The more complete and clean the data, the better the results.
Can you help me understand the results of a machine learning model?
Yes, explaining complex models in plain language is a core part of what I do. I'll make sure you and your team understand how the model works, what its predictions mean, and how to interpret its output, so it's not a 'black box' to you.