How do we know the model is better than what we do today?

We establish the current baseline first, then measure lift on data the model has not seen, and where possible run a controlled pilot before full rollout.

Do you build black boxes?

No. We favor models that can be explained to the people who use them, and provide explanations for individual predictions when decisions affect customers.

What about ongoing maintenance?

Models are monitored for drift and retrained on a schedule or trigger, either by your team with our runbooks or by us under a managed arrangement.

How is data science as a service different from staff augmentation?

The team is managed, has a lead accountable for outcomes and comes with our methods, templates and quality standards. You get a capability, not a set of resumes.

What happens to the work if we end the engagement?

Everything is documented and yours: code, models, notebooks and dashboards, with a handover plan to your team.

Data Science & Engineering

Data Science & Predictive Modeling

Forecasting, churn, risk and optimization models, delivered by embedded data science teams.

Talk to an engineer

See related work

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Overview

Descriptive analytics tells you what happened. Advanced analytics tells you what is likely to happen next and what to do about it: which customers will churn, how much to stock, which claims to review, where to send the crew. We build the models and, just as importantly, the way they enter the decision.

Our data scientists combine statistics, machine learning and domain knowledge, and they work alongside engineers so models move from notebook to production instead of living in slides.

This page also covers data science as a service: an embedded team of data scientists working on your backlog rather than a one-off project.

Offering 1

Advanced analytics and predictive modeling

Advanced analytics tells you what is likely to happen next and what to do about it. We build the models and the way they enter the decision, moving from notebook to production.

Forecasting

Demand, revenue, capacity and workforce forecasts with confidence ranges and scenario analysis.

Customer analytics models

Churn, propensity, lifetime value, segmentation and next-best-action.

Risk and fraud models

Credit, claims, operational and transaction risk scoring with explainability.

Optimization

Pricing, inventory, scheduling and routing using ML together with operations research.

Typical use

Demand and inventory planning for retail and distribution
Churn prevention programs for subscription businesses
Credit and claims decisioning support

Offering 2

Data science as a service

Hiring a full data science team is slow and hard to get right on the first try. Data Science as a Service gives you a steady, embedded team of data scientists and ML engineers who work on a cadence you set.

Two analysts reviewing dashboards at a workstation

Embedded data science teams

Dedicated scientists and engineers working in your tools, meetings and priorities.

Analysis on demand

Deep dives, ad hoc questions and decision support for leadership and operations.

Model development and maintenance

A backlog of predictive and optimization models built, deployed and kept accurate.

Flexible skill mix

Adjusting between data science, analytics engineering, ML engineering and visualization as the roadmap evolves.

Typical use

Mid-sized companies that need data science without building a department
Enterprises augmenting an in-house team for a program or backlog surge
Product teams that need continuous analysis and modeling support

Want this for your product? Talk to an engineer who has built it.

Book a working session

How we work

How we deliver

01
Frame
The decision, its economics and the baseline the model must beat.
02
Explore
Data assessment, feature discovery and quick baselines to gauge potential.
03
Model
Rigorous modeling, validation on holdout periods and explanation for stakeholders.
04
Deploy
Integration into workflows, monitoring and a review cadence with the business.

Stack

Tools we work with

We are neutral on tooling and pick what fits your environment, your team and the cost you can sustain.

Python, R, SQL
scikit-learn, XGBoost, LightGBM, statsmodels, Prophet
PyTorch for deep learning where warranted
OR-Tools and optimization solvers
MLflow
Databricks, Snowflake, BigQuery
Jupyter and Databricks notebooks
Power BI, Tableau, Looker
dashboards for model outputs

Bring the problem. We bring the team.

Talk to an engineer

Industries

Where we apply this most

Shopper checking her phone while picking fruit in a grocery store

Retail & E-commercePersonalization, demand forecasting, search and customer data platforms.Explore

Financial charts layered over a blue city skyline

Banking, Financial Services & InsuranceDocument intelligence, risk models, fraud detection and modern digital banking experiences.Explore

Container terminal with cranes and stacked containers at sunset

Logistics & Supply ChainForecasting, route and inventory optimization, visibility platforms and control towers.Explore

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HR Tech & StaffingCandidate matching, recruiter copilots, workforce analytics and ATS/HRMS platforms.Explore

FAQ

Questions we hear often

Related services

Next step

Ready when you are.

Tell us what you are trying to build and we will come back with a point of view, not a pitch.

Start a conversation

Data Science & Predictive Modeling

Advanced analytics and predictive modeling

Forecasting

Customer analytics models

Risk and fraud models

Optimization

Data science as a service

Embedded data science teams

Analysis on demand

Model development and maintenance

Flexible skill mix

How we deliver

Frame

Explore

Model

Deploy

Tools we work with

Where we apply this most

Questions we hear often

You may also need

Machine Learning, Vision & MLOps

Analytics, BI & Customer Insights

Data Strategy, Architecture & Governance

Ready when you are.