Separate, manage, and extract value from data that sits on your systems. Hire data scientists who develop recommendation engines that increase revenue and improve customer satisfaction. Work with professionals who turn data into decisions.
Fill out the form, and leave the rest to our dedicated experts.
Solve real business problems with data scientists who turn your data into real-time insight that moves operations.
Customer churn prediction
Demand forecasting models
Credit risk assessment
Fraud detection systems
Sentiment analysis on customer reviews
Text classification and topic modeling
named entity recognition
Object detection and image classification
Facial recognition systems
Medical image analysis
Quality control defect detection
Collaborative filtering algorithms
Content-based recommendations
Hybrid recommendation engines
Real-time personalization
A/B test design and analysis
Causal inference modeling
Bayesian statistics applications
Hypothesis testing frameworks
Sales and revenue forecasting
Demand planning models
Anomaly detection in metrics
Seasonal decomposition analysis
Data science projects can drift from their scope when you’re not tracking the right insights. We monitor your operations across phases to ensure the workflow operates as intended. Hire data scientists who deliver.
We track accuracy, precision, recall, F1 scores, and AUC-ROC, depending on your use case, to ensure your model performs beyond testing.
Validate data completeness, consistency, and accuracy to catch issues before they affect your results and overall decision-making.
We document every feature - what it is, why it matters, and how it's calculated to ensure team members have accurate insights at any time.
We launch models with version-controlled code, documented experiments, and tracked model artifacts that match reproducibility standards.
Our data scientists optimize metrics to align with business KPIs, including revenue, conversion rates, and customer retention.
Track model predictions, data drift detection, and performance alerts in real time so you know when operations aren’t aligned.
Regular updates on progress, clear explanations of technical decisions, and transparent reporting for zero surprises post-launch.
We check for bias in training data and model outputs to deliver machine models that work seamlessly throughout the workflow.
Different businesses have different requirements for a data scientist. At Uncanny, we understand it better than anyone else. We offer hiring models that fit your specific situation and project scope. Choose models that make sense.
The time-and-materials model offers maximum flexibility for businesses whose projects are still exploratory. Pay for the data science hours and resources dedicated to your project at a pre-agreed hourly or daily rate.
Need someone embedded in your team for ongoing work? Our dedicated data scientist model gives you a data scientist (or entire team) who works exclusively on your problems as a member of the team.
If you've got a well-defined problem with clear success metrics, our fixed scope model gives you cost certainty from day one. We agree on the deliverables, timeline, and budget up front to avoid surprises or overruns

Our data scientists dig into your datasets to uncover patterns, spot anomalies, identify correlations, and understand distributions. We use statistical analysis and visualization techniques to figure out what's actually in your data before building any models. This groundwork prevents wasted effort on models that were never going to work.
Raw data rarely works well in models without transformation. We create new features from existing data, handle categorical variables properly, normalize and scale where needed, and select the features that actually contribute to predictive power. Good feature engineering often matters more than the choice of algorithm.
We test multiple modeling approaches to find what works best for your specific problem. Our data scientists track experiments carefully, compare results objectively, and choose models based on performance, not hype. We focus on delivering models that work for your business and ensure complete efficiency since day one of operation.
Getting a model to work is one thing; aligning it to your operations is another. We fine-tune hyperparameters, optimize decision thresholds, handle class imbalance, and squeeze out performance gains that make a real difference. Small improvements in model accuracy can mean big differences in business impact.
At Uncanny, we believe in working with the best and have helped numerous brands reform their data operations. Check out our latest testimonials to find out more about our role in reforming data for big brand


We’ve helped numerous organizations across different niches build data infrastructure that delivers a competitive advantage. Check out our latest case studies to get an idea of our approach to data engineering.
Ready to connect with our data scientists? We receive numerous questions around our data science service every day. Here are some frequently asked questions to stay up to date on common queries and prepare for the call.
We build with deployment in mind from day one. That means writing production-ready code, setting up monitoring dashboards, testing models on real data distributions, and creating fallback logic for edge cases. Our models go live and stay live.
Absolutely. We integrate with whatever stack you're already using; whether that's cloud warehouses like Snowflake or Redshift, on-premise databases, data lakes, or a mix of systems. We adapt to your environment rather than forcing you to change everything.
That's more common than you'd think. We can help with data-labeling strategies, use semi-supervised or unsupervised approaches, or start with a smaller labeled dataset and expand from there. Messy data is part of the job.
We're pragmatic about it. Sometimes a pre-trained model or existing service solves 80% of your problem, and custom work handles the rest. Sometimes you need fully custom models. We recommend whatever actually makes sense for your situation and budget.
We test for bias across demographic groups, audit training data for representation issues, use fairness metrics alongside accuracy metrics, and document decisions around sensitive features. If bias exists, we catch it before deployment and work to mitigate it.
