Every hard problem in business is fundamentally a data science problem.
Virtual Data Scientist. Gather the facts. Instrument the process. Ask the right questions. Get state-of-the-art answers. With our expert help you can run your own data science team, powered by AI: forecasts with a range, who is ready to buy or drifting, fraud and bots flagged early, built on your own data and scored against what really happened last year.
For the CEO, COO, or head of revenue with no dedicated data science team, yet
The sales forecast lives in several spreadsheets, with macros. Account managers first realize a customer is leaving when they cancel a contract. Fraud shows up first in the chargeback report. Marketing and engineering need to optimize your shopping path - in real time and now while the market is hot. The call center is overcapacity again. These are all fundamentally data science problems. A Diagnostic finds the one that pays back first.
90% of data science is... data engineering.
Gathering the right data, instrumenting the right actions, building the data at the correct unit of measurement are all data science. So are the 'error bands' around the forecast, and the cycle times in the factory. They all require data. We start with a Diagnostic: we inventory the available features, trace them back to their origins, and deploy new telemetry.
Define the problem, train the model, evaluate the results.
The science is about curiosity, applied mathematics, some statistics, selecting or designing an algorithm. Science is about evaluating experiments and modeling the world with limited data. We've built teams and done this work solo, late at night. Data science is about using data to apply science to solve real-world problems. We then dive into the problem - orient, and observe, build and train a model of the decision space.
Then we evaluate the results, test the hypothesis, and repeat as needed. Measurement and ongoing re-evaluation, before and after production, are built in at the very beginning as fundamental.
A model predicts. A person decides.
Every prediction feeds a decision: who to staff, what to buy, which ad to run, what engineering builds next, what a customer sees. We wire the model into the systems, processes and people that make that decision.
Some decisions happen in real time. The model runs in production: a payment is held, a lead is routed, a page is ranked, and the customer never notices. Others need a person in the loop. The score lands in a queue or a weekly list, and your process and your people's judgment take it from there.
Dashboards were yesterday's interface. Today an AI agent reads and models the data on request, shows its sources, and hands the decision back. Your people are assisted, and always in charge.
An agent is a model. Measure it like one.
Machine learning with a large language model in the middle is still machine learning. Every prompt is a feature set. Every answer can be measured.
We evaluate agents the way we evaluate any model: a test set built from your own questions, scorers for accuracy and grounding, and the same checks run on a sample of live traffic. When the data is wrong, the agent is wrong, so data quality checks run in the same pipeline.
We organize the data and the know-how so an agent is efficient and correct: gold tables and Unity Catalog functions for the numbers, a knowledge graph and a document index for the context, and skills and harnesses that encode how your team works. MCP servers connect it to the tools your people already use.
Data scientists do more of this work than ever. They use AI to build, train and evaluate models, and they decide what gets measured. We've built it.
What we have built before, and can show you
Spam detection and mitigation for major US wireless carriers: ranking billions of calls in near real time. Full MLOps moved model releases from every 18 months to monthly and lifted detection from near coin-toss to over 90% precision and recall.
At a national ed-tech safety provider, threat detection: email and social streams scored in near real time to surface threats to schools and students before they emerge. One Databricks platform for pipelines, data science and model serving, with MLOps, for 31,000 school districts.
Twelve years at Microsoft (Bing, Office). Built Microsoft AI products: PowerPoint Designer and Word Editor. Co-inventor, Microsoft patent US 10,839,149 B2, machine learning shipped inside a Microsoft product.
On the business side: twelve years at Microsoft, lead data scientist to the CSO and CFO. Senior data leader in telecom and ed-tech. MBA, Economics, Entrepreneurship and Technology. MS, Data Science.
We build it, with our network of experts: senior engineers, data scientists and security partners we have worked with for years, brought in when the work needs them.
Bring the number you wish you could see coming.
In a free discovery session we look at your data, pick the first model, and say what it would take. You leave with a plan, not a proposal. Not sure which model comes first? The DataMind Diagnostic scores your options on your own data, for a fixed fee.