Data Scientist

Role: Data Scientist
Position Type: Full-Time Contract (40hrs/week)
Contract Duration: 1 year
Work Schedule: 8 hours/day (Mon-Fri)
Work Time: CST
Location: 100% Remote (Candidates can work from anywhere in LATAM Countries)

Core Responsibilities (What They'll Actually Do)

• Work hands-on in Databricks

• Collaborate closely with engineers

• Build and iterate on ML models for prediction and optimization

• Apply:

  • Neural networks

  • Decision trees

  • Classical optimization techniques

• Help kickstart new ML projects

• Support early work on AI agents for design applications (nice to have, not required)

• Interface with:

  • Vendor models

  • Internal ML pipelines

• Contribute to ML Ops concepts (but not heavy engineering)

What This Role Is NOT (Very Important)

• Not an ML Engineer

• Not FastAPI / backend-heavy

• Not a pure PhD / research scientist

• Not a production-infra owner

• Not primarily time-series modeling

• Not someone living only in notebooks with no engineering context

Ideal Background (Clear Signal from the Call)

Strongly Preferred

• Engineering background → Data Scientist

• Classical engineering disciplines (Mechanical or Chemical is okay, but prefers data engineer)

• Someone who converted into data science

• Comfortable with:

  • ML fundamentals

  • Optimization problems

  • Real-world physical systems

• Domain intuition > academic theory

Explicitly Not Looking For

• "Pure” PhD data scientists

• Research-only profiles with no applied context

Time Series Clarification

• Time series = value add

• Not a required skill

• Nice if they've seen sensor / operational data

• Do not block candidates for lack of deep forecasting experience

This reinforces:
They want applied ML + engineering thinking, not a forecasting specialist.

Skill Breakdown (Use This for Screening)

Must-Haves

• Hands-on experience working in Databricks

• Building ML models (not just evaluating outputs)

• Neural networks + tree-based models

• Optimization exposure (even classical methods)

• Comfortable partnering with engineers

• Practical, applied mindset

Nice-to-Haves

• Some AI agent exposure (Databricks flavor is a bonus)

• Some ML Ops familiarity (conceptual, not infra-heavy)

• Light API awareness (but not FastAPI ownership)

• Oil & gas domain exposure (cementing, production, operations)