Hire AI Data Scientists Who Build Models That Work
Build your AI team with nearshore data scientists from Latin America who turn data into business value. Start interviewing in 5 days while saving 50-60% compared to US hiring, with zero compromise on quality or collaboration.
Senior AI Data Scientists Ready to Join Your Team
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Why Hire AI Data Scientists Through Tecla?
5-Day Average Placement
Most recruiting firms take 6+ weeks to find AI talent. We match you with qualified data scientists in 5 days because we maintain a pre-vetted pool of 50,000+ developers.
Top 3% Acceptance Rate
We accept 3 out of every 100 applicants. You interview data scientists who've shipped production ML models generating real business value, not people who just completed online courses.
Save 60% on Salaries
Senior AI data scientists in Latin America cost $75K-$120K annually versus $180K-$260K+ in US tech hubs. Same expertise in deep learning, ML pipelines, and production deployment.
97% Retention After Year One
Our placements don't bounce after six months. Nearly all clients keep their AI data scientists past year one, proving we match technical skills and culture properly.
Zero Timezone Hassle
Stop waiting overnight for model results. Your data scientists work 0-3 hours different from US time, joining standups and iterating on experiments during your workday.
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What Our Clients Are Saying
"We needed a data scientist who understood both the math and the business impact. Tecla connected us with someone who had built similar recommendation systems at scale. She improved our model accuracy by 28% and explained it clearly to stakeholders."
"Traditional recruiting sent us candidates with great resumes but no production ML experience. Tecla's vetting was thorough. The data scientist we hired had deployed models handling millions of predictions daily and knew how to monitor them properly."
"Our forecasting models were constantly breaking in production. The AI data scientist from Tecla rebuilt our pipeline with proper monitoring and automated retraining. Forecast accuracy improved 40% and we stopped getting surprised by model drift."
Real Work Our AI Data Scientists Handle Daily
Hire AI Data Scientists in 4 Simple Steps
Tell Us What You Need
Review Pre-Vetted Candidates
Interview Your Top Choices
Hire and Onboard
What is an AI Data Scientist?
An AI data scientist builds machine learning models that solve business problems using data. Think of them as the bridge between raw data and automated decision-making. They don't just analyze data, they build systems that learn from it.
The difference from traditional data analysts? AI data scientists write code that trains models, deploy those models to production, and measure their real-world impact. They combine statistics, programming, and business judgment to decide which problems ML can actually solve.
These professionals sit at the intersection of software engineering, statistics, and domain expertise. They're not just running algorithms from libraries. They're choosing appropriate methods, validating assumptions, and building systems that work reliably with new data.
Companies hire AI data scientists when they're moving from manual analysis to automated prediction, scaling personalization beyond human capability, or optimizing decisions that happen too fast for human intervention. The role evolved as ML moved from research labs into production systems powering real products.
When you hire AI data scientists, you automate decisions that currently require human judgment at scale. Most companies see 20-40% improvements in key metrics through better prediction, personalization systems that actually convert, and operational efficiency from optimizing processes humans can't track.
Running ML experiments in notebooks that never reach production? Good data scientists build deployment pipelines, monitor model performance, and set up automated retraining. Your models improve continuously instead of becoming stale six months after launch.
Here's where the ROI becomes obvious. Manual credit risk assessment approving 100 applications per day? ML models review 10,000 per day with lower default rates. Product recommendations based on category browsing? Personalized models increase conversion 30-50%. Inventory planning based on last year's numbers? Forecasting models adapt to trends and reduce waste.
Your data team builds great dashboards but can't predict what happens next? Data scientists build models that forecast demand, predict churn, identify high-value leads, or detect fraud before it impacts revenue. These aren't insights, they're automated actions.
Your job description either attracts engineers who've built production vector search systems or people who followed a LangChain tutorial once. Be specific enough to filter for actual Chroma experience and real RAG implementation.
What Role You're Actually Filling
State whether you need RAG pipeline development, vector database optimization, or full-stack AI integration. Include what success looks like: "Reduce answer latency to under 200ms for 95th percentile queries" or "Improve retrieval precision from 0.6 to 0.8+ within 90 days."
Give real context about your current state. Are you migrating from Pinecone? Building your first RAG system? Scaling from 100K to 10M embeddings? Candidates who've solved similar problems will self-select. Those who haven't will skip your posting.
Must-Haves vs Nice-to-Haves
List 3-5 must-haves that truly disqualify candidates: "2+ years production experience with vector databases," "Built RAG systems handling 1M+ queries/month," "Optimized embedding pipelines reducing latency by 50%+." Skip generic requirements like "strong Python skills." Anyone applying already has those.
Separate required from preferred so strong candidates don't rule themselves out. "Experience with Chroma specifically" is preferred. "Experience with any production vector database (Chroma, Pinecone, Weaviate, Milvus)" is required.
Describe your actual stack and workflow instead of buzzwords. "We use FastAPI, deploy on AWS ECS, run async embedding jobs with Celery, and do code review in GitHub. Daily standups at 10am EST, otherwise async communication in Slack" tells candidates exactly what they're walking into.
How to Apply
Tell candidates to send you a specific RAG system they built, the retrieval metrics before/after their optimizations, and the biggest technical challenge they solved. This filters for people who've shipped actual systems versus those who played with notebooks.
Set timeline expectations: "We review applications weekly and schedule technical screens within 5 days. Total process takes 2-3 weeks from application to offer." Reduces candidate anxiety and shows you're organized.
Good interview questions reveal production experience versus academic knowledge.
Technical Depth Questions
What it reveals: Strong candidates start with understanding the business objective, not jumping to algorithms. They discuss defining success metrics, checking if ML is needed, data exploration, baseline models, evaluation strategy, and deployment considerations. Listen for business thinking, not just technical skills.
What it reveals: This separates people who understand ML fundamentals from those who just run libraries. Good answers explain underfitting (high bias) versus overfitting (high variance), mention cross-validation for detection, and discuss regularization, ensemble methods, or getting more data as solutions.
What it reveals: Experienced data scientists immediately mention precision, recall, F1, ROC-AUC, and why accuracy fails with imbalanced data. They connect metrics to business context (false positives versus false negatives having different costs). Watch for understanding that model evaluation depends on the use case.
Problem-Solving Questions
What it reveals: Practical candidates check for data drift (input distribution changed), concept drift (relationships changed), data quality issues, or bugs in the pipeline. They mention monitoring systems that would catch this early. This shows production ML thinking versus just training models.
What it reveals: Strong answers discuss transfer learning, semi-supervised methods, active learning to label strategically, data augmentation, or simpler models that work with less data. Avoid candidates who immediately suggest "just get more data" without exploring technical solutions.
Experience & Judgment Questions
What it reveals: Their war stories show what they've learned. Good answers explain specific failures (train-test data leakage, distribution shift, latency issues, incorrect business logic) and what they did to fix it. Candidates without production experience struggle here.
What it reveals: Experienced data scientists acknowledge simpler methods often work better. They discuss when deep learning makes sense (unstructured data, complex patterns, lots of data) versus when it's overkill. This reveals judgment about tool selection, not just knowing buzzwords.
Collaboration & Communication Questions
What it reveals: Good answers mention SHAP values, feature importance, example predictions, ablation tests showing impact, or building trust through consistent A/B test wins. They understand their job includes making models understandable, not just accurate.
What it reveals: This shows whether they've actually deployed models. Common answers include latency requirements, handling missing features at inference time, versioning, monitoring, and different languages (Python notebooks versus production Java). Listen for collaborative approach.
Cultural Fit Questions
What it reveals: Neither answer is wrong. But if you need production ML and they only want research, that's a mismatch. If you're building research capabilities and they only want proven methods, also a mismatch. Watch for self-awareness about preferences.
What it reveals: Strong candidates discuss recommending simpler approaches when appropriate, knowing ML isn't always the answer. They give examples of using rules, heuristics, or simple statistics instead of complex models. This shows business judgment over technical ego.
True Cost to Hire AI Data Scientists: LATAM vs. US
US hiring carries overhead most companies underestimate. Beyond salary, you're covering health insurance, retirement matching, payroll taxes, PTO, administrative expenses, and recruiting fees.
US Full-Time Hiring: Hidden Costs
- Health insurance: $10K-$15K
- Retirement contributions: $8K-$11K (401k matching)
- Payroll taxes: $11K-$15K (FICA, unemployment)
- PTO: $7K-$10K (accrued time off)
- Administrative costs: $5K-$8K (HR, payroll processing)
- Recruitment costs: $13K-$22K (agency fees, time-to-hire)
Total hidden costs: $54K-$81K per professional
Add base compensation and you're looking at $184K-$261K total annual investment per professional.
LATAM Hiring Through Tecla (Per Professional, Annually)
All-inclusive rate: $90K-$115K annually
This covers everything: compensation, benefits, payroll taxes, PTO, HR administration, recruiting, vetting, legal compliance, and performance management. No hidden fees, no agency markup, no administrative burden.
The Real Savings
Hiring nearshore AI Data Scientists cuts costs without cutting quality. US total: $184K-$261K per data scientist. Tecla rate: $90K-$115K.The difference is $69K-$171K saved per data scientist, representing 38-66% cost reduction. A five-person team costs $920K-$1.305M in the US, $450K-$575K through Tecla.That's $345K-$855K in annual savings with identical ML modeling expertise and real-time collaboration. During the first 90 days, Tecla replaces resources at no additional cost if the fit isn't right.
Frequently Asked Questions
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Connect with senior AI data scientists from Latin America in 5 days. Same expertise, full timezone overlap, 50-60% savings.
