Nearshore data science services

Senior LATAM engineers who turn a business question into a model your team can act on, and tell you first whether your data can answer it, at 30-50% below US rates.

Talk to an Expert

18+

years delivering software
150+

senior engineers across LATAM
6-8h

daily overlap with US teams
6+

Fortune 500 & global brands trust us

Most data science projects are commissioned as “we should be doing something with AI” and end as a notebook nobody opened twice.

FusionHit starts from the decision you are trying to make and what it is worth getting right, then works backwards to whether your data can support it. Senior engineers across Latin America, working on your hours.

FusionHit agile team during a daily standup

Data readiness assessment

A short engagement that answers whether the data you have can support the question you are asking, before anyone commits to a model. Sometimes the answer is that it cannot yet, and that is worth knowing in week one.

Forecasting

Demand, revenue and capacity forecasts backtested against your own history, so you can see how the model would have performed rather than how it scores on a metric.

Customer analytics

Churn prediction, lifetime value and segmentation built from your behavioural data, delivered in a form your CRM and your marketing team can actually use.

Pricing and optimization

Models for pricing, inventory, routing and allocation, tied to the operational constraint that actually binds rather than to a theoretical optimum.

Experimentation and causal analysis

A/B tests designed and read properly, so you can distinguish what your change caused from what would have happened anyway.

Model deployment and monitoring

Models put into your systems with drift monitoring and a retraining schedule, because a model that was right at launch and wrong by autumn is the normal outcome without it.

FEASIBILITY

Whether your data can answer the question

Every provider on this search starts from the models they build. None of them asks the question that decides the project, so we ask it first and it takes days rather than months.

Is the answer even recorded?

You cannot predict churn if nobody wrote down when customers left, and you cannot forecast demand from orders that were never captured as time series. The outcome you want to predict has to exist in the history, and surprisingly often it does not.

Is there enough history, and is it still relevant?

Models learn from the past, so the past has to be long enough to contain the pattern and recent enough to still describe the business. Three years of data from before a pricing change may be worse than none.

What would the model have to beat?

Every problem already has an answer: a rule, a spreadsheet, or somebody experienced guessing. That is the baseline, and if a model cannot beat it by enough to change a decision, we tell you before you fund it rather than after.

Why FusionHit

Why US companies choose FusionHit for data science

Senior engineering talent

Vetted nearshore engineers with production experience across industries and modern stacks.
Real-time collaboration

Nearshore teams aligned with US time zones for synchronous planning, reviews, and delivery.
Flexible engagement

Scale engineering capacity up or down as your roadmap and priorities evolve.
Integrated partnership

We work as an extension of your engineering and product teams, not as a detached external vendor.

FusionHit senior nearshore engineers working with a US client team

Talk to an Expert

Fintech

Credit risk, fraud patterns and portfolio behaviour modelled where the decision has to be explainable to a regulator.

Healthcare

Utilization, readmission risk and capacity planning modelled under HIPAA, with clinicians involved in what counts as a useful prediction.

SaaS

Churn, expansion and product usage modelled per tenant, tied to the actions your team can actually take in response.

Logistics

Demand, routing and ETA models built against the constraints operations really works under.

Retail

Assortment, pricing and inventory models tied to margin rather than to volume alone.

Manufacturing

Quality prediction and maintenance models built from sensor history, with a real cost attached to a false alarm.

Languages & libraries

Deep learning

MLOps

Platforms

Orchestration & transformation

Cloud

Staff augmentation

Senior data scientists added to your existing team, under your direction, in days rather than months.

Dedicated development teams

A team working only on your analytics, sprint after sprint, while you set the priorities.

Full project outsourcing

You hand over the question and we deliver it under our management, covering feasibility, modelling, deployment and monitoring.

How much do data science services cost?

Senior nearshore rates run 30-50% below equivalent US salaries and consultancy fees. What you pay depends on how much data preparation the problem needs, how many models, and whether deployment and monitoring are in scope. A feasibility engagement is short and cheap by design, and you get a line-item estimate on the first call.

How do we know whether we have enough data?

That is what the feasibility step is for, and it takes days. The questions are whether the outcome you want to predict was actually recorded, whether the history is long enough and recent enough to still describe your business, and whether the inputs you would need are available at the moment the prediction has to be made. If the answer is no, you find out for the price of an assessment.

What if the answer is that we are not ready?

We tell you, and we tell you what would make you ready — usually a change in what gets captured rather than a large project. Plenty of engagements start as data science and turn into three months of data engineering, which is the honest sequence rather than a change of subject.

How is this different from your machine learning services?

This page is analytics for decisions: forecasting, segmentation, experimentation, statistical modelling that a person acts on. When the model has to run continuously inside a product, that is machine learning engineering and it lives with our AI team. Most engagements start here and some graduate there.

How do you measure whether the model is any good?

Against the baseline you already have, which is usually a rule, a spreadsheet or an experienced person's judgement, and against the business metric the model is supposed to move. Accuracy on a holdout set is a check, not a result.

What happens to the model after it ships?

It gets monitored for drift and retrained on a schedule agreed before launch. A model is accurate against the world it was trained on, and the world moves. Anyone who hands you a model without a monitoring plan has handed you a future problem.

Can you explain how the model reaches its answers?

Yes, and for regulated decisions it is a requirement rather than a preference. We choose model types with the explainability the decision needs, which sometimes means a simpler model that people will actually trust and use over a marginally better one nobody can defend.

Can you work with our analysts and data team?

Yes, and it works better that way. Your team knows what the data means and where it lies, which is the part no external group can supply. We work in your environment and hand over code your team can read.

Who owns the models and the code?

You do. Notebooks, training code and deployment artefacts live in your repositories from the first commit, built on open-source tooling, and the contract assigns all intellectual property to you.

How do you handle sensitive data?

Analysis runs in your environment wherever possible rather than in ours, with masking or pseudonymization for fields the model does not need in the clear. For HIPAA, PCI DSS and SOC 2 scope we keep the evidence as we go rather than reconstructing it at audit time.

How fast can data scientists start?

We present matched profiles within days, you interview and approve them, and most engineers onboard in under 2 weeks. A feasibility assessment can often start sooner.

How much time zone overlap will we have?

Our engineers work across Latin America on US Central and Eastern time, which gives 6-8 hours of daily overlap. It matters here because most of the work is asking your team what a field means and whether a pattern in the data is real or an artefact of how it was collected.

Ready to find out if your data can answer it?

Tell us the decision you are trying to improve, and we will tell you whether your data supports it before anyone builds anything.

Trusted by leading teams

Rated by our clients

Nearshore data science services

Senior engineers who start from the decision, not the algorithm

Data science services we deliver

Data readiness assessment

Forecasting

Customer analytics

Pricing and optimization

Experimentation and causal analysis

Model deployment and monitoring

Whether your data can answer the question

Is the answer even recorded?

Is there enough history, and is it still relevant?

What would the model have to beat?

Why US companies choose FusionHit for data science

Senior engineering talent

Real-time collaboration

Flexible engagement

Integrated partnership

Models that changed a decision

Making 130,000 Financial Documents Searchable with AI

Embedding AI Across the Full Engineering Lifecycle

AI-Augmented Development and Operations in a Multi-Team Enterprise

Bring us the question and we will tell you if the data can answer it.

The questions each industry actually asks