Data Science for Decisions That Drive Growth
Deploy analytics and ML models that answer critical business questions with data pipelines engineered for accuracy and speed.
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Trusted and top rated tech team
Predictive models that drive decisions
Growing companies collect massive datasets but extract minimal insight beyond basic reporting. Predictive analytics uncover patterns in customer behavior, forecast demand, identify churn risk, and optimize pricing before competitors act. Our teams build models that integrate with existing systems and deliver recommendations your business units trust.
Our capabilities include:
- Customer behavior prediction and segmentation
- Revenue forecasting and demand planning
- Churn detection and retention modeling
- Recommendation engines and personalization
- Anomaly detection and fraud prevention
- Pricing optimization and market analysis
Who we support
We work with organizations where business questions require predictive capabilities but lack the expertise to build reliable models that integrate with existing operations and deliver actionable insights.
SaaS Companies Optimizing Growth
Your roadmap includes personalization, churn prediction, or demand forecasting. Manual analysis can't scale with customer data volume, and you need models that predict behavior patterns before competitors identify the same opportunities.
Enterprises With Untapped Data Assets
You collect operational and customer data across multiple systems but extract minimal strategic value. Predictive analytics reveal hidden patterns in purchasing behavior, operational efficiency, and market trends that drive competitive advantage.
Financial Services and E-commerce
You need fraud detection, risk scoring, dynamic pricing, or recommendation engines that adapt to changing patterns. Rule-based systems miss evolving customer behaviors, and prediction accuracy directly impacts revenue and retention metrics.
Ways to engage
We offer a wide range of engagement models to meet our clients’ needs. From hourly consultation to fully managed solutions, our engagement models are designed to be flexible and customizable.
Staff Augmentation
Get access to on-demand product and engineering team talent that gives your company the flexibility to scale up and down as business needs ebb and flow.
Retainer Services
Retainers are perfect for companies that have a fully built product in maintenance mode. We'll give you peace of mind by keeping your software running, secure, and up to date.
Project Engagement
Project-based contracts that can range from small-scale audit and strategy sessions to more intricate replatforming or build from scratch initiatives.
We'll spec out a custom engagement model for you
Invested in creating success and defining new standards
Why choose Curotec for data science?
1
Extraordinary people, exceptional outcomes
Our outstanding team represents our greatest asset. With business acumen, we translate objectives into solutions. Intellectual agility drives efficient software development problem-solving. Superior communication ensures seamless teamwork integration.
2
Deep technical expertise
We don’t claim to be experts in every framework and language. Instead, we focus on the tech ecosystems in which we excel, selecting engagements that align with our competencies for optimal results. Moreover, we offer pre-developed components and scaffolding to save you time and money.
3
Balancing innovation with practicality
We stay ahead of industry trends and innovations, avoiding the hype of every new technology fad. Focusing on innovations with real commercial potential, we guide you through the ever-changing tech landscape, helping you embrace proven technologies and cutting-edge advancements.
4
Flexibility in our approach
We offer a range of flexible working arrangements to meet your specific needs. Whether you prefer our end-to-end project delivery, embedding our experts within your teams, or consulting and retainer options, we have a solution designed to suit you.
Data science implementation capabilities
Customer Behavior Prediction
Revenue Forecasting Models
Recommendation Systems
Anomaly Detection
Demand Planning Analytics
Pricing Optimization
Tools & technologies for data science
Machine Learning Frameworks
Our data scientists use ML libraries and frameworks that provide pre-built algorithms, training tools, and deployment capabilities.
- scikit-learn — Python library for customer segmentation, churn prediction, and demand forecasting with classification, regression, and clustering algorithms
- TensorFlow — Deep learning framework for complex pattern recognition in customer behavior, fraud detection, and recommendation systems with neural networks
- PyTorch — ML framework for building custom predictions with flexible architecture that adapts to unique business problems and data structures
- Keras — High-level API that accelerates development for forecasting, classification, and anomaly detection with rapid prototyping capabilities
- XGBoost & LightGBM — Gradient boosting frameworks for accurate predictions on structured business data including sales forecasts and risk scoring
- Hugging Face Transformers — Pre-trained language tools for sentiment analysis, customer feedback classification, and text-based recommendation systems
Data Processing & Analysis
Python libraries and distributed computing frameworks manage data manipulation, analysis, and feature engineering for training preparation.
- Pandas & NumPy — Python libraries for cleaning customer data, calculating business metrics, and preparing datasets for predictive training
- Apache Spark — Distributed framework for processing large transaction histories, customer databases, and operational data across multiple systems at scale
- Dask — Parallel computing library that handles datasets larger than memory for customer segmentation and historical analysis without infrastructure changes
- Apache Hadoop — Distributed storage and batch processing for analyzing years of transactional data, logs, and customer interaction records
- SciPy & Statsmodels — Statistical libraries for hypothesis testing, trend analysis, and regression that validate business assumptions with data
- Feature-engine & FeatureTools — Automated feature creation tools that extract predictive signals from customer behavior, purchase patterns, and usage data
Analytics Platforms
Statistical analytics tools build forecasting capabilities, time series analysis, and classification for business predictions.
- Prophet — Facebook’s forecasting tool for time series prediction including sales forecasting, demand planning, and seasonal trend analysis
- Statsmodels — Python library for econometric analysis, regression testing, and statistical validation of business hypotheses with confidence intervals
- ARIMA & SARIMA — Time series algorithms for revenue forecasting, inventory prediction, and detecting seasonal patterns in customer behavior data
- RandomForest & Decision Trees — Interpretable classification algorithms for credit scoring, customer segmentation, and risk assessment with feature importance rankings
- Scikit-learn Pipelines — Automated workflows that preprocess data, train algorithms, and generate predictions with consistent methodology across business units
- H2O.ai — AutoML platform that builds and compares multiple algorithms automatically for classification, regression, and ranking business problems
Visualization & Business Intelligence
Visualization tools turn model predictions into interactive dashboards and reports that business teams use for strategic decisions.
- Tableau — Visual analytics platform with interactive dashboards that display prediction results, customer segments, and forecast accuracy for stakeholders
- Power BI — Microsoft’s business intelligence suite that connects prediction outputs to operational reports with natural language queries and Office integration
- Matplotlib & Seaborn — Python visualization libraries for creating statistical charts, distribution analysis, and correlation heatmaps that explain prediction behavior
- Plotly — Interactive graphing library for building dynamic dashboards that show real-time predictions, trends, and drill-down analysis capabilities
- Apache Superset — Open-source BI platform for exploring prediction results, comparing performance metrics, and sharing insights across business teams
- Looker — Data platform that embeds predictive analytics into existing workflows with SQL-based queries and centralized metric definitions
Cloud ML & AutoML Services
Managed cloud platforms provide scalable compute, pre-built algorithms, and automated model training that accelerates project delivery.
- AWS SageMaker — Managed ML service with Jupyter notebooks, built-in algorithms, and one-click deployment for building customer prediction systems
- Google Cloud AI Platform — End-to-end ML platform with Vertex AI, AutoML capabilities, and pre-trained algorithms for common business analytics use cases
- Azure Machine Learning — Microsoft’s cloud ML service with automated selection tools, drag-and-drop designer, and deployment to Azure infrastructure
- Databricks — Unified analytics platform combining data processing, collaborative notebooks, and MLflow for tracking experiments across business units
- Google AutoML — Automated machine learning that builds custom predictions for classification, forecasting, and recommendations without extensive ML expertise
- DataRobot — Enterprise AutoML platform that automates feature engineering, algorithm selection, and deployment for predictive analytics at scale
Model Monitoring & Governance
Tracking systems monitor prediction accuracy, detect drift, and maintain audit trails for compliance and ongoing model performance.
- MLflow Tracking — Experiment logging system that records parameters, performance metrics, and predictions for comparing forecasting accuracy
- Weights & Biases — Monitoring platform that tracks performance over time, visualizes prediction trends, and alerts teams to accuracy degradation
- Evidently AI — Drift detection tool that identifies when customer behavior patterns change and predictions become less reliable
- Great Expectations — Data validation framework that ensures input data quality meets standards before feeding into production prediction systems
- Neptune.ai — Metadata store for tracking versions, comparing business impact metrics, and documenting decisions for audit requirements
- Seldon Alibi — Explainability toolkit that documents why predictions were made for regulatory compliance and stakeholder transparency
FAQs about our data science solutions
How long does implementing data science take?
Timelines depend on problem complexity and data readiness, typically 3-6 months from initial data assessment through production deployment. We start with focused use cases that deliver measurable value while building infrastructure for additional models.
Do we need a data science team in-house?
Not initially. We build and deploy models while training your teams on interpreting predictions and monitoring performance. Many organizations start with external expertise then gradually build internal capabilities as needs expand.
How do you keep predictions accurate over time?
We implement monitoring systems that track prediction accuracy, data drift, and pattern changes. Automated alerts trigger retraining workflows when performance degrades, maintaining reliability as customer behavior and market conditions evolve.
What data do we need to start predictive analytics?
Clean, historical data relevant to your business question – typically thousands of examples with consistent formatting. We assess data quality early, identify gaps, and build pipelines to prepare data before model development begins.
How do you measure data science ROI?
We define success metrics upfront – prediction accuracy, cost reduction, revenue impact, or time savings. Models are evaluated against baseline approaches, and production monitoring tracks actual business outcomes against initial projections.
Can data science integrate with our existing systems?
Yes, we build models that connect to your current databases, CRMs, and business applications through APIs. Predictions feed into existing workflows without requiring platform changes or disrupting operations.
Ready to have a conversation?
We’re here to discuss how we can partner, sharing our knowledge and experience for your product development needs. Get started driving your business forward.