Data Scientist Senior
CDMX, Miguel Hidalgo, Mexico
- Pay
- Salary not listed in the saved posting
- Work setup
Remote — work setup source
Work setup in the employer’s listing: Remote
From the employer’s posting- Employment
Full-time — employment source
Employment type in the employer’s listing: Full-Time
From the employer’s posting
What you’ll work on
Full postingYou will design, implement, and operate Machine Learning and AI models—including LLMs and agentic systems—to optimize our risk underwriting and customer management strategies.
Build predictive models for customer origination and management using alternative data such as e-invoicing
Run A/B tests, champion-challenger tests and causal inference experiments
Write Python code to track model drift and trigger retraining
Enforce model governance: documentation, validation, explainability and bias analysis
Explain technical findings to executives and cross-functional teams, and mentor junior data scientists
From the employer’s posting
You will design, implement, and operate Machine Learning and AI models—including LLMs and agentic systems—to optimize our risk underwriting and customer management strategies. You will drive continuous experimentation, monitor model performance, ensure strict governance compliance, and translate data into strategic business decisions while mentoring junior data scientists.
Build predictive models and ML algorithms for customer origination and management, leveraging alternative data sources like electronic invoicing
Run rigorous experiments (A/B testing, champion-challenger, causal inference)
Write scalable Python code to track model drift and trigger retraining
Enforce model governance standards, ensuring comprehensive documentation, validation, explainability, bias analysis, and regulatory compliance
Translate complex technical findings for executive leadership and cross-functional teams (Credit, Product, Business), and mentor junior data scientists
Education & alternatives
Required Profile • Education & Experience: BS in a quantitative STEM field with 5+ years of analytics experience (or Master's with 2+ years). Minimum 3 years of proven experience as a Data Scientist.. • Technical Stack: Advanced object-oriented Python (Pandas, scikit-learn), complex SQL, and Git. Experience extracting and analyzing large datasets (Big Data).. • ML & Statistics: Deep expertise in supervised/unsupervised models, deep learning, causal inference, and statistical fundamentals. Hands-on experience with LLMs, foundational models, and agentic systems via APIs or open-source tools. • Desirable: Financial sector experience operating Risk and Collections models, cloud platforms (AWS), and MLOps practices. Advanced English proficiency. Value Proposition
Tools in this posting
- Python
- SQL
- AWS
- pandas
- scikit-learn
Source — Tool mentions in context
Key Responsibilities • Model Development: Build predictive models and ML algorithms for customer origination and management, leveraging alternative data sources like electronic invoicing.. • Experimentation: Run rigorous experiments (A/B testing, champion-challenger, causal inference) and evaluate LLMs in the development lifecycle using quality evals.. • MLOps & Monitoring: Write scalable Python code to track model drift and trigger retraining. Partner with ML Engineers and Tech to automate CI/CD pipelines, versioning, and production deployments.. • Governance: Enforce model governance standards, ensuring comprehensive documentation, validation, explainability, bias analysis, and regulatory compliance.. • Communication & Mentoring: Translate complex technical findings for executive leadership and cross-functional teams (Credit, Product, Business), and mentor junior data scientists. Required Profile
Required Profile • Education & Experience: BS in a quantitative STEM field with 5+ years of analytics experience (or Master's with 2+ years). Minimum 3 years of proven experience as a Data Scientist.. • Technical Stack: Advanced object-oriented Python (Pandas, scikit-learn), complex SQL, and Git. Experience extracting and analyzing large datasets (Big Data).. • ML & Statistics: Deep expertise in supervised/unsupervised models, deep learning, causal inference, and statistical fundamentals. Hands-on experience with LLMs, foundational models, and agentic systems via APIs or open-source tools. • Desirable: Financial sector experience operating Risk and Collections models, cloud platforms (AWS), and MLOps practices. Advanced English proficiency. Value Proposition
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Oct 17, 2026 · 1d ago
- Recorded sightings
- 1
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
Job description
Who are we?
We are Mexico's leading financial technology company, empowering over 100,000 clients to achieve their business goals. Our mission is to fuel the growth of small and medium-sized enterprises (SMEs) through innovative solutions—including financing, corporate credit cards, and payment systems. We partner with entrepreneurs to help them overcome challenges, transforming their businesses into true engines of economic growth while contributing to the development of our community, our country, and the planet.
About the Role
You will design, implement, and operate Machine Learning and AI models—including LLMs and agentic systems—to optimize our risk underwriting and customer management strategies. You will drive continuous experimentation, monitor model performance, ensure strict governance compliance, and translate data into strategic business decisions while mentoring junior data scientists.
Key Responsibilities
• Model Development: Build predictive models and ML algorithms for customer origination and management, leveraging alternative data sources like electronic invoicing..
• Experimentation: Run rigorous experiments (A/B testing, champion-challenger, causal inference) and evaluate LLMs in the development lifecycle using quality evals..
• MLOps & Monitoring: Write scalable Python code to track model drift and trigger retraining. Partner with ML Engineers and Tech to automate CI/CD pipelines, versioning, and production deployments..
• Governance: Enforce model governance standards, ensuring comprehensive documentation, validation, explainability, bias analysis, and regulatory compliance..
• Communication & Mentoring: Translate complex technical findings for executive leadership and cross-functional teams (Credit, Product, Business), and mentor junior data scientists.
Required Profile
• Education & Experience: BS in a quantitative STEM field with 5+ years of analytics experience (or Master's with 2+ years). Minimum 3 years of proven experience as a Data Scientist..
• Technical Stack: Advanced object-oriented Python (Pandas, scikit-learn), complex SQL, and Git. Experience extracting and analyzing large datasets (Big Data)..
• ML & Statistics: Deep expertise in supervised/unsupervised models, deep learning, causal inference, and statistical fundamentals. Hands-on experience with LLMs, foundational models, and agentic systems via APIs or open-source tools.
• Desirable: Financial sector experience operating Risk and Collections models, cloud platforms (AWS), and MLOps practices. Advanced English proficiency.
Value Proposition
• Impact at Scale: Build analytical solutions that directly shape credit risk assessment and drive core business value..
• Cutting-Edge Innovation: Lead the practical integration of Generative AI and emerging technologies to elevate our data science capabilities..
• Strategic Collaboration: Work closely with Credit, Product, and Tech teams to ensure technical viability aligns with real market needs..
• Culture of Excellence: Join a high-performance, C-Level-facing environment that values GRIT, critical thinking, strategic execution, and the responsible use of AI..
Your next step
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- Pay
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- Location & working pattern
CDMX, Miguel Hidalgo, Mexico
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