Data engineering · Data scientists

Hire Data Scientists from Canada, Latin America, and the US

Hire a data scientist who turns the data you already have into forecasts and models that drive pricing, risk, and operating decisions. We find the right person, or build your entire data team, in Canada, Latin America, or the US, on a contract, permanent, or fractional basis.

Compare salaries by market

Data scientists, screened by data scientists

DevEngine is a Vancouver-based IT recruitment and staffing firm. We recruit data scientists from Canada, Latin America, and the US for contract, permanent, or fractional roles. They build demand forecasts and churn and credit risk models, design and read out A/B tests, and prototype models for ML engineers to take into production. A senior data scientist screens every candidate before you see one. You manage the day-to-day work. We handle contracts, HR, and admin.

The role

A data scientist answers questions with models and experiments

A data scientist combines statistics, machine learning, and data visualization to find patterns in complex data, predict trends, and solve business problems that reporting alone can't. They frame the business question, explore the data, build and test models, and explain what the results mean and how confident the business can be in them.

Most businesses now hold more data than their teams have time to use. A data scientist turns it into decisions made on evidence, and made sooner: a forecast finance plans against, customer segments marketing can target, a churn model that flags accounts before they leave, or an experiment readout that shows whether a change paid off.

The role is often confused with two others, and organizations define all three differently. In general:

Data scientist

What they work on: Predictions, experiments, and models
Core question: What's likely to happen next, and did this change work?
Typical outputs: Demand and revenue forecasts, risk and churn models, A/B test readouts for stakeholders
Works most with: Product, finance, and marketing leads, plus analysts and ML engineers
Often brought in when: A decision needs a forecast or a tested result, not another report

Data analyst

What they work on: Questions about what happened and why
Core question: What do the numbers say, and what should we do about it?
Typical outputs: Dashboards, KPI reports, analyses
Works most with: Business leaders, operations, and finance
Often brought in when: Leaders need reliable answers from data they already have

ML engineer

What they work on: Models running in production
Core question: Will this model run reliably, at scale, inside our product?
Typical outputs: Model services, deployment pipelines, monitoring
Works most with: Software engineers, data scientists, and platform teams
Often brought in when: A model or AI feature has to ship and stay healthy

	Data scientist	Data analyst	ML engineer
What they work on	Predictions, experiments, and models	Questions about what happened and why	Models running in production
Core question	What's likely to happen next, and did this change work?	What do the numbers say, and what should we do about it?	Will this model run reliably, at scale, inside our product?
Typical outputs	Demand and revenue forecasts, risk and churn models, A/B test readouts for stakeholders	Dashboards, KPI reports, analyses	Model services, deployment pipelines, monitoring
Works most with	Product, finance, and marketing leads, plus analysts and ML engineers	Business leaders, operations, and finance	Software engineers, data scientists, and platform teams
Often brought in when	A decision needs a forecast or a tested result, not another report	Leaders need reliable answers from data they already have	A model or AI feature has to ship and stay healthy

If the need is reliable reporting, see Hire Data Analysts. If a model has to ship and run in your product, see Hire ML Engineers.

When to hire

When you need a data scientist

The need usually shows up as one of these situations:

Forecasts miss, and the business pays for it.
Demand, churn, and credit risk move faster than planning assumptions, so inventory, staffing, and revenue targets are set on last quarter's numbers instead of a forecast.
Pricing and growth decisions aren't tested.
Price changes, campaigns, and product launches go live without a controlled experiment, so leadership commits budget without knowing which initiatives actually moved revenue or margin.
Your data assets aren't producing a return.
The company has years of customer, transaction, and operational data, but no models that use it to reduce cost, detect risk, or show where revenue is growing.
AI funding depends on a proven business case.
Before approving a full AI build, leadership needs evidence that a model will deliver measurable value, and no one on the team can scope, validate, and present that case.

Need a different role?

If you need reliable reporting on what already happened, hire a data analyst. If you need a model to run in production, hire a machine learning engineer. We recruit for both roles.

Our screen

What our senior data scientists look for

Most candidates can build a machine learning model in Python. Fewer can show one that changed a business outcome and explain how they tested it: what they measured it against, and how they made sure it would hold up on new data. A senior data scientist screens every candidate on the decision, the method, the result, and what the business did with it.

Starting from the business decision
We ask how they turned a broad executive request into a question data could answer. Strong candidates define the decision and the success metric before choosing a method.
Results leadership can trust
We ask how they validated a model or experiment, and how they explained its uncertainty to executives who had to commit budget based on the result.
Measurable business impact
We ask for a model or analysis that changed a pricing, risk, or operational decision, and for the revenue, cost, or efficiency outcome that followed.
Fluency with your constraints
Experience with your industry's data, regulatory requirements, and governance standards changes how a data scientist works. We weight that context as heavily as degrees or certifications.

Candidates who lead with algorithms instead of business outcomes, can't defend how they validated a result, or have never seen their work adopted by the business are screened out. Your interviews focus on fit, not filtering.

Where we source

Hire from Canada, Latin America, or the US

One search can cover all three talent pools. Brief the role once, and compare candidates from each market side by side before you decide.

Canada

Where candidates are based: Across Canada
Working hours: Canadian time zones, Pacific to Atlantic
Cost: Canadian market rates. See the Canada salary guide below.
Fits when: The role needs knowledge of the North American market, regular in-person time in Canada, or a Canadian base

Latin America

Where candidates are based: Across Latin America, including Argentina, Brazil, Colombia, Mexico, and Costa Rica
Working hours: Within 2 hours of US Eastern time, year-round. Up to 4 hours ahead of Pacific time.
Cost: Typically 35–40% below comparable North American cost. See the Latin America salary guide below.
Fits when: You want a wider candidate pool or more seniority for the budget, or the data scientist will work with a nearshore team

United States

Where candidates are based: Across the United States
Working hours: US time zones, Pacific to Eastern
Cost: US market rates.
Fits when: The role needs knowledge of the US market, regular in-person time at a US site, or a US base

	Canada	Latin America	United States
Where candidates are based	Across Canada	Across Latin America, including Argentina, Brazil, Colombia, Mexico, and Costa Rica	Across the United States
Working hours	Canadian time zones, Pacific to Atlantic	Within 2 hours of US Eastern time, year-round. Up to 4 hours ahead of Pacific time.	US time zones, Pacific to Eastern
Cost	Canadian market rates. See the Canada salary guide below.	Typically 35–40% below comparable North American cost. See the Latin America salary guide below.	US market rates.
Fits when	The role needs knowledge of the North American market, regular in-person time in Canada, or a Canadian base	You want a wider candidate pool or more seniority for the budget, or the data scientist will work with a nearshore team	The role needs knowledge of the US market, regular in-person time at a US site, or a US base

Not sure which market fits? Tell us about the role, and we'll scope it in each market and show you candidates and cost side by side.

Technology salaries in Canada and Latin America

Benchmark salaries by role and seniority before you set a budget.

Download the Canada Salary Guide Download the Latin America Salary Guide

Engagement models

Contract, permanent, or fractional

Contract

Who employs the data scientist: DevEngine
Time commitment: Full-time, for a defined period
Fits when: A proof of concept, a forecasting model, or an experimentation program with a defined end
Placement protection: Two-week guarantee

Permanent

Who employs the data scientist: You
Time commitment: Full-time, ongoing
Fits when: Data science is an ongoing capability your teams rely on
Placement protection: 180-day prorated guarantee from the start date: full refund in the first 30 days, then partial refunds on a sliding scale

Fractional

Who employs the data scientist: DevEngine
Time commitment: Part-time, hourly or monthly
Fits when: A model review, an experiment design, or a few hours a week of advanced analysis, without a full-time seat
Placement protection: Tailored to the scope of work

	Contract	Permanent	Fractional
Who employs the data scientist	DevEngine	You	DevEngine
Time commitment	Full-time, for a defined period	Full-time, ongoing	Part-time, hourly or monthly
Fits when	A proof of concept, a forecasting model, or an experimentation program with a defined end	Data science is an ongoing capability your teams rely on	A model review, an experiment design, or a few hours a week of advanced analysis, without a full-time seat
Placement protection	Two-week guarantee	180-day prorated guarantee from the start date: full refund in the first 30 days, then partial refunds on a sliding scale	Tailored to the scope of work

Each model works a little differently depending on whether you hire in Canada, Latin America, or the US. Compare the options by market to find the engagement that fits your team.

Explore services by market

How it works

How a data scientist search runs

You brief us on the scope and goals of the project and your current data and machine learning stack.
We source for your role in Canada, Latin America, or the US, not from a bench.
A senior data scientist screens each candidate. Someone who does the same work, not a recruiter matching keywords.
You choose who to interview and who to hire.
You direct the work. On contract and fractional engagements, DevEngine holds the contract and handles payroll and compliance. On a permanent hire, the data scientist joins your payroll.
If the placement isn't working, we replace the candidate: within two weeks on contract, or under the 180-day prorated guarantee on a permanent hire.

FAQ

Data scientist hiring questions

How much does it cost to hire a data scientist?

Cost depends on four things: seniority, domain and method specialization, the market you hire from, and the engagement model. Hiring in Latin America typically costs 35–40% less than comparable North American hires. For salary benchmarks across technology roles, download the Canada Salary Guide or the Latin America Salary Guide. DevEngine quotes against your actual brief on the first call.

Which skills and certifications do data scientists bring?

Most work in Python or R, with SQL for querying relational databases. In Python, pandas and NumPy handle data manipulation and statistical analysis, and scikit-learn or TensorFlow handle machine learning. Beyond the tools, they bring strong grounding in statistics and experimental design, and many hold graduate degrees in a quantitative field. Certifications are less common than in engineering roles. Examples include the Microsoft Certified: Azure Data Scientist Associate. Tools, degrees, and certifications show method knowledge, but not whether someone can change a decision. That's what the senior data scientist screen tests.

Do we need a data engineer before hiring a data scientist?

In most cases, yes. When data is incomplete, inconsistent, or spread across several systems, a data scientist spends much of their time preparing it rather than building models. A data engineer puts reliable pipelines in place first, or works alongside the data scientist from the start. DevEngine can scope both roles together.

Can I hire a fractional data scientist?

Yes. A fractional data scientist works part-time, on an hourly or monthly cadence, inside your team and tools. Companies often use one for a model review, designing an experiment, or a proof of concept before committing to a full-time hire. DevEngine places fractional data scientists from Canada, Latin America, and the US.

Should I hire a data scientist from Canada, Latin America, or the US?

Companies often choose Canada or the US when the role needs local market knowledge, regular in-person time, or a base in that country. Latin America offers a wider pool and more seniority for the budget, with working hours within 2 hours of US Eastern. DevEngine can run the search across all three markets so you can compare candidates.

Can a data scientist work with a DevEngine Engineering POD?

Yes. A data scientist can sit inside an Engineering POD or work alongside one. You choose the POD's composition and direct the work. The data scientist frames the questions and builds the models, and the engineers turn them into products and pipelines on your sprint cadence, in your tools.

What's your interest?

Choose the path that aligns with how you want to build and scale your team.

Tell us the problem your data needs to solve.

We'll tell you which market and model fit, and how long the search should take.

Not ready to talk yet? Compare technology salaries in the Canada Salary Guide or the Latin America Salary Guide.