Data & Analytics

Data science & advanced analytics

Statistical modeling, forecasting, segmentation and experimentation that answer high-value business questions.

Vue d’ensemble

Some business questions need more than a dashboard: which customers are likely to leave, how demand will move next quarter, which price or promotion will perform best, or whether a change actually caused an improvement. Our data scientists apply statistical modeling and machine learning to answer questions like these with rigor.

We frame each problem with the business owner, agree how results will be used and choose the simplest method that answers the question reliably. Our work spans forecasting, segmentation, propensity and churn modeling, pricing and promotion analysis, optimization and causal inference. Results are communicated in plain language, with uncertainty made explicit rather than hidden.

Where an analysis needs to run repeatedly, we productionize it with our data engineering and ML engineering teams so it becomes a dependable part of decision-making. We also help establish experimentation practices, such as A/B testing, so your teams can measure the impact of changes with confidence.

Ce que vous obtenez

01Problem framing and analytical approach agreed with business owners
02Models and analyses with documented methods and assumptions
03Findings and recommendations communicated in business terms
04Productionized models or scheduled analyses where needed
05Experimentation framework and measurement guidance

Technologies que nous maîtrisons

Python
R
scikit-learn
statsmodels and Prophet
Databricks
Jupyter
Snowflake

Nous sommes indépendants des éditeurs : les outils sont choisis en fonction de vos besoins et de vos investissements existants.

Les questions que l’on nous pose souvent

How is data science different from your machine learning engineering service?

Data science focuses on answering business questions and building analytical models, while ML engineering focuses on running models reliably in production. The two teams work closely, and many engagements involve both.

How do you account for uncertainty in forecasts?

We report ranges and confidence levels alongside point estimates and explain the main drivers of uncertainty. That helps planners decide how much buffer or flexibility to build into their plans.