South helps growing companies find, hire, and pay top Latin American talent. Build high-performing teams in 21 days or less.
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Hire a quantitative analyst who builds the models that price risk, forecast revenue, and turn raw data into defensible decisions. South places pre-vetted quantitative analysts from Latin America who work in your US time zone and cost 30 to 60 percent less than a stateside quant, with placement in roughly two to four weeks and no large upfront fees. You get a dedicated, full-time analyst with the statistics, programming, and domain rigor to back your most important numbers.
A quantitative analyst is a specialist who applies advanced mathematics, statistics, and programming to model risk, pricing, forecasting, and other complex business problems. Often called a quant, they build, test, and validate the models that drive high-stakes decisions, from credit risk and fraud detection in fintech to revenue forecasting and pricing optimization in SaaS and enterprise.
The role grew up in finance, where quants priced derivatives and managed portfolio risk, but the skill set now powers any data-intensive business that needs more than descriptive reporting. A modern quantitative analyst might build a credit-scoring model for a lending product, design a fraud-detection system, develop a churn-prediction model for a subscription business, optimize pricing across customer segments, or construct a demand forecast that feeds inventory and finance planning. The common thread is rigor: a quant does not just find a correlation, they quantify uncertainty, validate the model out of sample, and stress-test the assumptions.
Quantitative analysts are heavy programmers. Python is the lingua franca, with libraries like pandas, NumPy, scikit-learn, statsmodels, and SciPy doing the work, and R remains common for statistical modeling. They are fluent in SQL for pulling and shaping data, and many work with time-series methods, Monte Carlo simulation, regression, and increasingly machine-learning techniques. They understand the math underneath, which is what separates a quant from an analyst who runs a model they do not fully grasp.
The role overlaps with several others but is distinct. A data scientist shares the toolkit but often spans a broader range of problems and production ML. A machine learning engineer focuses on deploying and scaling models in production systems. A financial analyst works in spreadsheets on planning and reporting rather than building statistical models. A quantitative analyst sits at the deep end of the analytical pool: strong on the mathematics and statistics, focused on modeling specific high-value problems, and accountable for the defensibility of the numbers. When the cost of being wrong is high, a quant is who you want building the model. The best ones are as careful about validation, assumptions, and edge cases as they are about the model itself, because in quant work an undocumented assumption is a future loss waiting to happen.
Hire a quantitative analyst when a decision worth real money depends on a model, and getting that model wrong is expensive. In fintech, that moment arrives when you launch a lending or credit product and need defensible underwriting, or when fraud losses justify a detection model. In SaaS and enterprise, the trigger is usually when forecasting, pricing, or churn becomes material enough that spreadsheet heuristics are not good enough and you need rigorous, validated models behind your planning.
Another trigger is regulatory or board scrutiny. When you have to defend how a number was produced, a quant gives you documented, validated methodology rather than a black box nobody can explain. A third is when your data team is producing dashboards but no one can build the predictive or risk models leadership keeps asking for. That gap, between describing the past and modeling the future, is exactly where a quantitative analyst lives.
Who should NOT hire yet: if your analytical needs are still descriptive, reporting on what happened, tracking KPIs, building dashboards, you do not need a quant yet. A data analyst will serve you better and cost less. Hiring an expensive quant to build dashboards wastes the talent and the budget. Likewise, if you do not yet have clean, reliable data, a quant will spend their time fighting your pipelines instead of building models. In that case invest in a data engineer first so the quant has a foundation to work from. And if you have exactly one modeling problem that is well understood and unlikely to change, a focused contract project may make more sense than a full-time hire. Bring on a quant when modeling is an ongoing, high-stakes need, not a one-off curiosity.
Probe the statistics, not just the tooling. Many candidates can call a scikit-learn function. Far fewer can explain why a model overfits, what a confidence interval actually means, or when a linear assumption breaks. Ask them to walk through how they validated a model and how they knew it would generalize. The strong ones talk about out-of-sample testing, cross-validation, and the specific ways their model could fail. The weak ones describe accuracy on training data as if it proves something.
Second, look for healthy skepticism about their own work. The best quants assume their model is wrong until proven otherwise. They stress-test assumptions, look for data leakage, and worry about edge cases. Ask about a time a model they built failed in production or in backtesting and what they learned. A candidate who has never seen a model break either has not built many or is not paying attention.
Third, confirm they can communicate. A brilliant model that leadership does not trust or understand is useless. Ask them to explain a technical result to a non-technical audience. You want someone who can translate uncertainty into a clear recommendation without either overstating confidence or hiding behind jargon.
Who should NOT hire yet: avoid the candidate who is all machine-learning buzzwords and no statistical foundation. If they reach for a deep neural network on a problem a logistic regression would solve more defensibly, they are optimizing for resume keywords, not for your business. Also be cautious of the academic profile with no exposure to messy real-world data or production constraints. Quant work in industry means imperfect data, deadlines, and stakeholders, and a candidate who has only worked with clean research data sets may struggle. You want rigor plus pragmatism, not one without the other.
Quantitative analysts are among the most expensive analytical hires in the US market. A US-based quant typically costs around 12,000 dollars per month in base salary, and senior quants in fintech or trading-adjacent roles command far more, before benefits, equity, and overhead. Fully loaded, a US quantitative analyst frequently runs well over 175,000 dollars a year, which puts a serious dent in any budget.
Through South, a comparably skilled quantitative analyst from Latin America generally runs around 5,650 dollars per month, a savings of roughly 53 percent. The gap is a function of labor markets, not capability. Latin America produces a large number of strong quantitative graduates from rigorous statistics, mathematics, economics, and engineering programs, and many have applied those skills to risk, fraud, and forecasting problems for US and global companies. Competitive local compensation in the region translates to a far lower cost for a US employer hiring the same talent.
The reason quality holds is that quantitative work is universal. A correctly validated model, a sound statistical method, and a well-documented assumption are equally valid whether produced in New York or Santiago. The mathematics does not change with the postal code. You are paying for analytical depth and programming skill, both of which LatAm has in abundance. Because South places dedicated full-time professionals rather than charging agency rates by the hour, you avoid markups and large upfront fees, and you pay a straightforward salary calibrated to a market where it goes much further. For a role this expensive in the US, the savings on a single quant can fund another hire entirely.
The time-zone overlap matters more for quants than people expect. Modeling is collaborative work: a quant needs to interrogate the business stakeholders who understand the problem, work with data engineers on the pipelines feeding the model, and present results to leadership. When your quant is in a Latin American time zone, all of that happens in real time during your business day. Most of the region overlaps US Eastern and Central time, so a question about a model assumption gets answered in the same afternoon, not the next morning.
The talent depth is genuine. Latin American universities have strong quantitative programs, and the region has produced a deep bench of statisticians, econometricians, and data professionals. Many have worked on credit risk, fraud, and forecasting for US fintechs and multinationals through nearshore teams, which means they arrive already familiar with the kinds of problems and the production constraints you face. English proficiency among quantitative professionals is strong, which is essential for a role that lives or dies on clear communication of uncertainty.
Cultural alignment reduces friction. LatAm professionals generally share US norms around directness, deadlines, and intellectual debate, which suits the kind of rigorous back-and-forth good quant work requires. Combined with the cost savings and time-zone fit, you get a dedicated, deeply skilled analyst who functions like an in-house quant at a fraction of the loaded cost. Because you own the relationship directly, your quant builds lasting knowledge of your data, your models, and your business, instead of rotating off mid-project the way a contractor or consultancy would.
South specializes in matching US companies with dedicated, full-time LatAm professionals, and quantitative roles are exactly where the value is largest given how expensive these hires are domestically. We begin by understanding the specific modeling problems you need solved, whether that is credit risk in a lending product, fraud detection, churn modeling, or revenue forecasting, along with your data stack and the seniority you need. From there we draw on a pre-vetted pool of quantitative talent and present a short list of candidates whose statistical depth, programming skills, and domain experience already fit.
Because candidates are screened for statistical rigor, Python and SQL proficiency, English fluency, and US-time-zone availability, most clients move from kickoff to a placed, full-time quant in about two to four weeks. There are no large upfront fees, and you own the relationship directly. Your quantitative analyst joins your team, learns your data and your models, and stays for the long term rather than churning like an agency placement.
If you are weighing whether you need a quantitative analyst, a data scientist, or a machine learning engineer to put models into production, we will help you scope the right hire before you commit. Ready to put rigorous, defensible models behind your most important decisions? Book a call with South and we will line up vetted quantitative analyst candidates in your time zone within days.
A US-based quantitative analyst typically costs around 12,000 dollars per month in base salary plus benefits and overhead. Through South, a comparably skilled quant from Latin America generally runs around 5,650 dollars per month, a savings of roughly 53 percent, with no large upfront placement fees.
Most placements move from kickoff to a signed, full-time quantitative analyst in about two to four weeks. Candidates are pre-vetted for statistical depth, Python and SQL skills, English fluency, and time-zone fit, so you interview finalists rather than screen a large pool.
Yes. South places quants who work US business hours. Most of Latin America overlaps with US Eastern and Central time, so your quantitative analyst is online to interrogate stakeholders, collaborate with engineering, and present model results in real time during your business day.
South's quants are vetted for Python (pandas, NumPy, scikit-learn, statsmodels), R, and SQL, plus core methods including regression, time-series analysis, Monte Carlo simulation, and model validation. Many also bring machine-learning depth and domain experience in risk, fraud, or forecasting.
A quantitative analyst focuses on rigorous, validated models for specific high-stakes problems like risk and pricing, with deep statistical grounding. A data scientist often spans a broader range of problems and production machine learning. The toolkits overlap; the focus and accountability differ.
You own the relationship directly. South places dedicated, full-time professionals who join your team and build lasting knowledge of your data and models. They are not rotating agency contractors or consultancy resources billed by the hour.
A quant builds and validates the model and partners closely with engineering to deploy it. For heavy production deployment and scaling, they work alongside a machine learning engineer. Many of South's quants have hands-on experience moving validated models into live systems.
The region has the perfect mix of everything you want in remote employees: English skills, shared time zones, hard-working, and depth of talent. They are already accustomed to working remotely for top US startups and Fortune 500 companies.
Absolutely! The US and Latin America have basically the same time zones. No Latin American city is more than two hours ahead of EST.
Every hire is sourced based on your exact needs. They will arrive ready to support your business right away. They can do basically any tasks done remotely, but we recommend starting them as support so your team has more bandwidth for high-value strategic tasks.
All types of roles - customer service, executive assistant, sales, accounting, email marketing, lead generation, content writers, operations, social media marketing, and more!
You can pay directly through us (most popular) or we can connect you with one of our payroll partners.
You don't have to deal with any American labor laws / taxes when hiring full-time remote contractors. They aren't US-based, so no visas or sponsorships to deal with either.
We recommend market pay which varies for each role. See our salary guide and success stories for some ideas.
Then, we have two different models:
Staffing (most popular) - We charge a small monthly fee for each employee's monthly salary to make the process hassle-free. The fee covers sourcing, recruiting, admin, payroll, compliance, ongoing support, and a free replacement if necessary at any point. There are no cancellation fees or minimum commitments. You only pay if you make a hire.
Headhunting - A one-time simple fee once we've found the perfect candidate. This comes with a 120-day replacement guarantee.
For both options, you only pay something if we find you someone great that you want to hire.
Yes, we only recruit for full-time and we strongly recommend full-time hiring if you can. Stability (full-time & long-term) is highly sought after abroad. The top caliber candidates are only looking for full-time work.
You're also going to spend time training and getting them up to speed on your processes. It would be a waste to do that over and over again with new people all the time.
We recommend training new hires on one thing at a time.
For example, once they get up to speed on lead generation, you can add the next role writing blog posts or whatever you'd like. You can definitely overlap roles until you have enough work for multiple people.
The cost of living is much less in Latin American countries. Many of our employees are able to own homes, raise families, provide for their parents, and have in-home help of their own with their salaries.
If you aren't happy with your hire in the first 120 days, we will work with you to conduct a second round of search for the same role for free.
Just email us at Hello@HireInSouth.com and we will get back to you with an answer as soon as possible.
