You Don't Need a Data Scientist. You Need an API Engineer.

Stop Hiring PhDs to Connect APIs

Companies burn cash hiring Data Scientists who want to write papers, not ship product. Unless you are training a model from scratch (you are not), you do not need a researcher. You need a builder.

The Reality

Modern AI is consumed via API (OpenAI, Anthropic, Google). The skill is "Systems Engineering," not "Calculus." You need someone who can:

1. Design reliable API integrations
2. Build data pipelines that feed models
3. Implement error handling and fallback logic
4. Optimize for cost and latency
5. Deploy and monitor production systems

None of these require a PhD.

The Mis-Hire Problem

Data Scientists are trained to experiment. They build models in notebooks. They optimize metrics on datasets. This is valuable when you are doing original research. It is not valuable when you need to ship a product that calls the OpenAI API.

What You Actually Need

• API Engineer: Builds the integrations, handles auth, manages rate limits, implements retries
• Prompt Designer: Crafts the system prompts and evaluates output quality (this can be the same person)
• DevOps/Infra: Deploys, monitors, and scales the system

This is a 2-3 person team that ships, not a 5-person research lab that publishes.

The Exception

If you genuinely need to fine-tune a model or train from scratch (extremely rare for most businesses), then yes, hire a data scientist. For the other 95% of use cases, hire engineers.

Our team consists of engineers who know how to ship, not just research. We have built production AI systems across multiple industries and can deliver faster than a new data science hire can finish their onboarding.