Nearshore Data Science Services

Technical Questions

• • Design an A/B testing framework for an e-commerce platform. How would you determine sample size, handle multiple testing, and ensure statistical rigor?
• • You're building a churn prediction model for a subscription business. Walk me through your approach: feature engineering, model selection, evaluation metrics, and how you'd deploy it.
• • How would you approach analyzing causality vs. correlation in observational data? What techniques would you use to establish causal relationships?
• • Explain how you'd build a recommendation system for a marketplace with cold start problems. What algorithms would you consider?
• • You have a dataset with significant class imbalance (1:100 ratio). How would you approach modeling this? What metrics would you optimize for?
• • Walk me through your experience with time series forecasting. What methods have you used (ARIMA, Prophet, LSTM) and when would you choose each?
• • How do you communicate complex statistical findings to non-technical stakeholders? Can you give a specific example?

Cultural Fit Questions

• • Tell me about a data science project where your initial hypothesis was wrong. How did you pivot and what did you learn?
• • Describe a situation where you had to balance statistical rigor with business timelines. How did you make tradeoffs?
• • How do you approach stakeholder management when your analysis reveals inconvenient truths about the business?
• • When you discover a data quality issue in production, what's your process for investigating and fixing it?

Current Trends

• Data science roles grew 64% year-over-year in LATAM as companies move from descriptive analytics to predictive modeling. Demand for causal inference and experimentation expertise increased 89%.
• ML skills are now baseline: 87% of data scientist postings require production ML experience, up from 54% in 2023. Pure BI/analytics roles are declining.
• Domain expertise commands significant premiums: data scientists with vertical knowledge (fintech, healthcare, e-commerce) earn 25-35% more than generalists.

Demand Factors

• U.S. shortage of data scientists with business acumen: 38,000+ open positions vs. 16,000 candidates with 3+ years of stakeholder-facing experience.
• LATAM data scientists bring bilingual communication advantage: 82% have B2+ English proficiency, critical for explaining complex findings to executive teams.
• Real-world experience at scale: LATAM data scientists have built models for companies processing billions in transactions, demonstrating capabilities matching top U.S. tech companies.

15,000+ Talent Pool

Access our curated database of senior LATAM professionals. Every candidate is pre-screened for English (B2+), technical skills, and remote work readiness.

AI Screening (Stage 1)

Our AI analyzes your requirements and screens 15,000+ candidates against tech stack, timezone, experience, and culture fit. Only 500 pass to the next stage.

Human Expert Review (Stage 2)

Senior recruiters conduct live interviews verifying bilingual communication (English/Spanish), technical depth, and culture fit. Only the top 3% make it to your shortlist.

48h Shortlist

Receive 3-5 AI & human vetted profiles with video intros, code samples, and detailed assessments. Schedule interviews directly with top candidates.

Offer Management

We handle salary negotiations, contract setup, and compliance. You focus on evaluating fit—we handle the paperwork and logistics.

Risk-Free Start

Start with a paid trial period. If the hire doesn't work out, we replace them at no cost. 95% of our placements convert to long-term hires.