Data science services — senior data scientists, time & material
When the question is “what does this data actually tell us,” the answer comes from a data scientist with real analytical depth — not a BI analyst pulling predefined reports. The engineers on this platform do statistical work, build predictive models, and hand you analysis that drives decisions.
What these data scientists do
They work on problems that require statistical rigor and domain judgment, not just dashboard configuration. That includes:
- Designing experiments and analyzing results (A/B tests, multivariate tests, holdout groups)
- Building predictive models from scratch using your historical data
- Identifying patterns in customer behavior that aren’t visible in aggregate metrics
- Quantifying uncertainty — giving you a confidence range, not just a point estimate
- Delivering analysis that answers a specific business question, not a general exploration
The deliverable is usually a combination of a working model or statistical output, a reproducible codebase, and a plain-English write-up that a non-technical stakeholder can act on.
Disciplines covered
Predictive modeling — regression, classification, and ensemble methods applied to forecasting, scoring, and ranking problems.
A/B testing and experimentation — experiment design, sample size calculation, statistical significance testing, sequential analysis. Including setting up the infrastructure if you don’t have it.
Customer segmentation — k-means, hierarchical clustering, RFM models. Used for marketing targeting, product personalization, and cohort analysis.
Churn modeling — survival analysis, binary classification models trained on behavioral signals. Includes defining what “churn” means for your product, which is often where these projects get stuck.
Demand forecasting — time series analysis for inventory, revenue, and capacity planning. Includes decomposition, seasonality modeling, and anomaly detection.
NLP on unstructured data — topic modeling, sentiment classification, entity extraction applied to customer feedback, support tickets, survey responses.
Typical team compositions
Single data scientist engagement — one senior data scientist working on a focused analytical problem. Common for companies that have a data platform but lack the person to do advanced analysis on top of it.
Data scientist + data engineer — when the analytical work requires cleaning, transforming, or pipeing data that isn’t already in a usable form. The data engineer handles the plumbing; the data scientist handles the analysis.
Data science team — two or more data scientists working on parallel tracks, often with a team lead who owns prioritization and stakeholder communication. Used when a company wants to build out a data science function without going through a 6-month hiring process.
Tech stack
| Area | Technologies |
|---|---|
| Core languages | Python, R, SQL |
| ML libraries | scikit-learn, XGBoost, LightGBM, statsmodels |
| Big data | Apache Spark, Databricks |
| Statistical computing | R (tidyverse, lme4, survival), SciPy |
| Experimentation | Python (statsmodels, pingouin), internal tooling |
| Visualization | Matplotlib, Seaborn, Plotly, ggplot2 |
| Data platforms | Snowflake, BigQuery, Redshift, Databricks |
How billing works
You rent the data scientist. We are the employer of record — you don’t deal with Romanian or Bulgarian employment law, tax registration, or equipment provisioning. The engineers work in your tools, on your data, under your direction.
Billing is hourly, settled monthly. You can scale a team up for a heavy analytical sprint, then reduce hours during a quieter period. Most clients run 160–200 hours per data scientist per month at the start of a project, then step down to 80 hours for ongoing analytical support.
You own all analysis, models, and code produced during the engagement.
Related pages
Get a team estimate
Tell us what analytical problems you’re working on and we’ll suggest a team configuration and monthly cost.