Data Science Services
When measurement is solved, prediction becomes possible
Machine learning, recommendation engines, and operations research that automate decisions across your organization. We build models grounded in your data and deploy them where they drive measurable impact.
Book a Meeting
What We Build
Recommendation Systems
Collaborative filtering, content-based, and hybrid recommender engines for e-commerce, affiliate marketing, and media. From product recommendations to publisher-offer matching, we build systems that increase engagement and revenue.
Operations Research
Inventory optimization, route optimization, and resource allocation using linear programming, simplex methods, and constraint-based algorithms. We solve complex logistical problems that spreadsheets cannot handle.
Predictive Analytics
Demand forecasting, churn prediction, lead scoring, and conversion modeling. Time-series models and classification algorithms that tell you what will happen next and what to do about it.
Customer Intelligence
RFM segmentation, behavioral modeling, and lifetime value prediction. We turn transaction data into dynamic customer segments that power personalized marketing and retention campaigns.
Anomaly Detection and Fraud Prevention
Real-time anomaly detection for financial transactions, conversion rate monitoring, and quality control. ML models that catch issues before they become costly.
Optimization and Automation
Budget allocation, campaign optimization, commission renegotiation, and process automation. Models that replace manual decisions with scalable, data-driven logic.
TESTIMONIALS
Witanalytica has been an excellent partner in managing and optimizing our Tableau environment. Their team’s technical expertise and proactive support have streamlined our reporting processes, improved dashboard performance, and provided valuable insights to our business. Their responsiveness and deep understanding of data analytics make them a trusted extension of our own team.
Mark Lack
Director of Data Analytics and AI, The Ubique Group
Witanalytica helped us transition from Excel to a dynamic dashboard, allowing us to view all the relevant data and the KPIs that we track as a business. Instead of having our developers code an interface for weeks, we can now instantly accomplish this process through an interface, eliminating the need for manual coding.
Radu Albastroiu
Startup Founder, masinilacheie.ro
Witanalytica’s expertise in big data engineering and visualization complements our digital media audit and customer analytics services. Collaborating with them allows us to deliver end-to-end analytics solutions and services, without the risks and investments associated with building these capabilities in-house.
Silviu Toma
Senior Partner, Microanalytics
Working with Witanalytica has transformed our approach to reporting. Their expertise in PowerBI enabled us to go beyond the limited capabilities of Excel, allowing us to provide our clients with dynamic and visually captivating PowerBI dashboards. This capability has facilitated rapid testing, iteration, and the collection of customer feedback to improve our platform.
Alin Rosca
Startup Founder, RepsMate
Working with Witanalytica has been a consistently positive experience. They are responsive, professional, and approach every revision with patience and precision. What sets them apart is a strong understanding of supply chain management, inventory planning, and sales operations, which makes collaboration efficient and ensures deliverables align with real business needs. They have also worked effectively across multiple departments in our organization and manage a 6-7 hour time zone difference seamlessly. I would confidently recommend them to any organization seeking a skilled and dependable analytics partner.
Rubin Chen
Supply Chain VP, The Ubique Group
Your Goals, Our Expertise
We start from your strategic objectives and work our way back to the right mix of solutions and technologies, not the other way round.
Book a Consulting CallFrom Problem to Production
We start by understanding the business decision you want to automate. Then we audit available data sources, assess quality, and identify gaps before writing a single line of code.
We establish a measurable baseline, engineer features from raw data, and select the right algorithmic approach: classification, regression, clustering, or optimization.
Iterative model training with train/test splits, cross-validation, and bias detection. We prioritize interpretability alongside accuracy so stakeholders trust the output.
Production-grade deployment as APIs, batch pipelines, or embedded logic. Models connect directly to your dashboards, CRM, ERP, or operational systems.
Ongoing performance monitoring, data drift detection, and scheduled retraining. Models degrade over time -- we keep them accurate and relevant.
We start by understanding the business decision you want to automate. Then we audit available data sources, assess quality, and identify gaps before writing a single line of code.
When Do You Need Data Science Services?
- Your dashboards tell you what happened, but not what will happen next.
- Manual decisions around pricing, inventory, or targeting are becoming bottlenecks.
- You have enough historical data but no models extracting value from it.
- You need to optimize routes, stock levels, or resource allocation under constraints.
- Customer churn, fraud, or quality defects are costing you money that prediction could prevent.
- You want to personalize recommendations, offers, or content at scale.
Why Hire Witanalytica for Data Science?
Measuring Framework First
We never deploy ML before the measuring framework proves exactly where it is needed. Dashboards validate the problem; models solve it.
Operations Research Expertise
Inventory optimization, route planning, and resource allocation using linear programming, simplex methods, and constraint-based algorithms, not just off-the-shelf ML.
Proven on Real Data
RFM segmentation deployed for e-commerce clients, ABC velocity analysis for 3PL logistics, recommendation engines for affiliate networks, and demand forecasting for supply chains.
Interpretable Models
We prioritize models that stakeholders can understand and trust. Feature importance, decision boundaries, and confidence intervals are part of every delivery.
Full-Stack Integration
Models are only useful when connected to decisions. We deploy as APIs, embed in dashboards, or feed directly into CRM, ERP, and marketing automation systems.
Continuous Model Health
Production models degrade as data changes. We monitor accuracy, detect drift, schedule retraining, and alert when intervention is needed.
Our Data Science Pricing Models
Transparent pricing built for long-term partnerships, not one-off transactions.
On-Demand Expertise
All tasks are tracked, and the corresponding invoice of the delivered services is billed monthly.
| Activity | Hourly Rate |
|---|---|
| Data Engineering & Database Administration | $110 |
| Business Intelligence Reporting | $90 |
| Data Science | $120 |
Reserved Capacity Agreement
- Pre-purchase a package of monthly working hours that guarantees reserved capacity and priority availability, regardless of our workload.
- Because this capacity is exclusively allocated to you, unused hours do not carry over to the following month.
| Hours Package | Price |
|---|---|
| Every 50 hours | $4,500 10% savings |
Alternatively, we also offer project-based pricing
For well-defined engagements, we scope the full project upfront and agree on a fixed fee, so you know exactly what to expect.
Tools and Platforms
Python ML Stack
scikit-learn, TensorFlow, PyTorch, XGBoost, LightGBM, statsmodels, Prophet, NetworkX, and SciPy for optimization and operations research
Amazon SageMaker
We build and deploy ML models at scale on Amazon Web Services using SageMaker for end-to-end machine learning workflows.
Azure ML Studio
Our team has hands-on experience with Microsoft Azure ML Studio for automated ML, responsible AI tooling, and enterprise ML workflows.
Google Cloud AI
We design and deploy ML solutions on Google Cloud using BigQuery ML, Vertex AI, and AutoML for training models directly on your data warehouse.
Databricks
Unified analytics platform combining data engineering and data science. MLflow for experiment tracking, model registry, and deployment.
Case Studies for Data Science Services
Explore real life case studies and see how we delivered measurable outcomes in similar situations.
Showing 3 case studies
Inventory Lot Size Optimization for a Global Industrial Manufacturer
How a global manufacturer freed working capital and recovered warehouse space by optimizing SAP lot sizes using demand variability analysis and a repeatable Alteryx-to-Tableau analytics workflow.
Read case study →
SAP Inventory Optimization: Reducing Consignment Exposure
An optimization model built with Alteryx and R helped a manufacturing plant reconcile SAP stock while minimizing consignment exposure.
Read case study →
Marketing Automation with RFM Segmentation for a Coffee Chain
How we helped a coffee shop chain connect POS data, build RFM segmentation, and automate SMS campaigns that reduced churn by 10% and grew revenue 12%.
Read case study →Related Articles
Explore insights and guides related to our data science practice.
11 articles
AI Readiness Checklist: Assess Your Data Maturity First
Most AI projects fail due to poor data foundations. Use this data maturity checklist to assess whether your organization is truly ready to deploy AI.
Identity Resolution: Unifying Customer Data for Marketing
Customer data scattered across CRM, email, ads, and web analytics? Identity resolution unifies fragmented profiles for precise targeting and attribution.
Recommendation Systems: What Executives Need to Know Before Investing
A strategic guide to recommendation systems for business leaders. Learn how they work, where they create value beyond e-commerce, and how to evaluate whether your organization is ready to build one.
LLMs for Business Leaders: Applications Across Departments
LLMs go beyond chatbots. Learn how business leaders apply them to customer service, marketing automation, HR workflows, and internal knowledge management.
Generative AI in Advertising: Hyper-Personalized Campaigns
Generative AI enables individually tailored ad creative at scale. Explore how it changes campaign design, audience targeting, and creative production.
LLMs for Data Analytics: Extracting Business Insights with AI
LLMs do more than generate text. See how they integrate with analytics workflows to surface patterns, automate reporting, and speed up decision-making.
Generative AI Hyper-Personalization: Beyond Segmentation
Generative AI enables one-to-one personalization at scale. Explore how it challenges segment-based marketing and transforms targeting and customer experience.
Structuring a Data Science Department: 3 Org Models Compared
Building a data science team? Compare embedded, centralized, and hybrid structures to find which model fits your company size, culture, and analytics maturity.
RFM Segments: Using Customer Behavior to Define High-Value Groups
Not all customers have equal value. Learn how to use purchase frequency, recency, and monetary data to define RFM segments and prioritize your marketing spend.
Data-Driven Segmentation for Personalized Marketing Campaigns
RFM segments reveal who your customers are. Learn how to turn those segments into targeted campaigns with personalized messaging, offers, and timing.
RFM Analysis Explained: Segment Customers by Value and Behavior
RFM analysis groups customers by recency, frequency, and monetary value. Learn how to build segments, interpret scores, and apply them to marketing strategy.
Data Science FAQs
Business intelligence focuses on descriptive analytics: what happened and why. Data science goes further with predictive analytics (what will happen) and prescriptive analytics (what should we do). ML models automate decision-making that would be impossible with dashboards alone.
Demand forecasting and inventory optimization for supply chain and retail.
Customer segmentation and churn prediction for marketing and retention.
Lead scoring and conversion prediction for sales teams.
Route optimization and resource allocation for logistics and operations.
Fraud detection and anomaly monitoring for finance and e-commerce.
Recommendation engines for product, content, or offer personalization.
You have enough historical data to train predictive models.
Manual decisions are becoming bottlenecks in your operations.
Your BI dashboards show what happened, but you need to know what will happen next.
You want to automate repetitive analytical decisions at scale.
Operations research uses mathematical optimization to solve logistics, allocation, and scheduling problems. We use it for inventory optimization (optimal stock levels, reorder points, safety stock), route optimization (minimizing delivery costs under constraints), and resource allocation (workforce scheduling, budget distribution). These are constraint-based problems that ML alone cannot solve efficiently.
A proof-of-concept model can be built in 2-4 weeks. Production-grade ML pipelines with monitoring and retraining typically take 2-3 months depending on complexity and data availability.
It depends on the problem. RFM segmentation works with a few thousand transactions. Recommendation systems need enough user-item interactions to find patterns. Deep learning requires large volumes. We assess data availability during discovery and recommend the right technique for what you have.
Rigorous ML engineering practices: train/test splits, cross-validation, feature importance analysis, and bias detection during development. In production, we monitor prediction accuracy, detect data drift, and schedule automated retraining when performance degrades.
Yes. We deploy models as REST APIs, embed predictions in Power BI or Tableau dashboards, connect to CRM systems like Salesforce or HubSpot, and feed outputs into marketing automation platforms. The model is only useful if it reaches the people making decisions.
We offer two engagement models with transparent pricing.
On-Demand Expertise
All work is tracked and billed monthly at hourly rates:
- Data Engineering & Database Administration - $110/hr
- Business Intelligence Reporting - $90/hr
- Data Science - $120/hr
Reserved Capacity Agreement
- Pre-purchase a 50-hour monthly package at $4,500 (10% savings)
- Guaranteed priority availability regardless of our workload
We also offer project-based pricing for well-defined engagements.
Contact us to discuss the best fit for your needs.