Description

Senior Data Scientist

About The Role

We are looking for an experienced Senior Data Scientist to join Ria’s Data Science team and help turn advanced machine learning solutions into reliable products that deliver measurable business impact.This is a hands-on role that combines Data Science, Machine Learning, product thinking, and ML operationalization. You will develop and validate models while owning the Data Science side of their journey from experimentation to production readiness, launch, monitoring, adoption, and continuous improvement.

You will partner closely with Product, Engineering, Data Engineering, MLOps, and business teams. While Engineering and MLOps own infrastructure and deployment execution, you will ensure models are analytically sound, production-ready, measurable, and continuously improving.We're looking for someone who combines strong technical expertise with business judgment, ownership, and the ability to translate complex analytical work into practical solutions.

What You'll Do

Machine Learning & Data Science

- Develop, validate, and improve supervised and unsupervised machine learning models aligned with business objectives.
- Perform exploratory data analysis, statistical testing, experimentation, causal analysis, and model validation.
- Build decision engines and rule-based systems to automate and optimize business decisions.
- Design reusable analytical workflows and scalable Data Science frameworks.
- Define success metrics and connect model performance to operational and business outcomes.
- Use Python, PySpark, SQL, and big-data technologies to work with complex datasets at scale.

ML Product Operationalization

- Own the Data Science readiness of ML solutions moving into production, from validation and acceptance criteria through post-launch monitoring.
- Translate model logic into clear production requirements for scoring pipelines, APIs, features, thresholds, explainability, and fallback behavior.
- Partner with Engineering and MLOps on production releases while maintaining ownership of analytical validation and model behavior.
- Define and monitor model health, including data quality, feature drift, prediction drift, model effectiveness, and business outcomes.




- Validate feature availability, freshness, training-serving consistency, and batch or real-time scoring requirements.
- Investigate unexpected model behavior and coordinate corrective actions with Engineering, MLOps, and business teams.
- Create feedback loops that use business outcomes, overrides, and user feedback to guide retraining, threshold calibration, and model improvements.
- Maintain appropriate model documentation, versioning, validation records, and governance standards.

Collaboration & Business Impact

- Work closely with Product Managers, Engineers, Data Engineers, MLOps specialists, and business stakeholders to turn business requirements into scalable analytical solutions.
- Support user acceptance testing and controlled production rollouts.
- Define KPIs and dashboards that demonstrate the impact of ML solutions.
- Communicate technical findings and recommendations clearly to both technical and non-technical audiences.
- Enable adoption by helping users understand model recommendations, limitations, and expected outcomes.
- Mentor junior Data Scientists and promote strong standards around experimentation, reproducibility, documentation, and responsible AI.

What We're Looking For

Required Qualifications

- 7+ years of professional experience in Data Science, Machine Learning, or a related field.
- Advanced degree such as an MS, PhD, or MBA in Data Science, Computer Science, Statistics, Mathematics, Operations Research, Engineering, or another quantitative discipline.
- Strong hands-on experience developing supervised and unsupervised machine learning models.
- Strong programming skills in Python and PySpark.
- Advanced SQL skills and experience working with relational and non-relational data.
- Experience with Spark, Hadoop, or similar big-data technologies.
- Experience working in cloud environments, particularly AWS.




- Hands-on experience taking ML solutions from development through production readiness, release validation, monitoring, and continuous improvement.
- Experience monitoring deployed models and identifying data quality, feature drift, prediction drift, and model performance issues.
- Understanding of production ML concepts such as acceptance criteria, model versioning, feature consistency, monitoring, rollback procedures, and operational handoffs.
- Experience using Git and collaborative software development practices.
- Excellent written and verbal communication skills in English.
- Strong ability to prioritize, work independently, and operate effectively in a fast-paced environment.
- Ability to travel when required.

Preferred Qualifications

Experience in one or more of the following areas would be an advantage:

- Production ML pipelines and MLOps practices, including automated testing, CI/CD, model registries, monitoring, and retraining workflows.
- Cloud platforms such as AWS, Azure, or GCP.
- Payments, financial services, credit decisioning, or fraud detection.
- Data governance, privacy, model explainability, and responsible AI.
- Cross-functional product teams and adoption of analytical products by business users.
- AI-assisted development tools such as GitHub Copilot or Claude Code.

What Success Looks Like

In this role, success means more than building accurate models. You will help ensure that Data Science solutions:

- Solve meaningful business problems.
- Successfully transition from experimentation into production.
- Remain reliable and measurable after launch.
- Can be understood and adopted by business teams.
- Continuously improve through monitoring, feedback, and new data.
- Deliver measurable operational and business value.

What We Offer

- A collaborative, innovative, and international work environment.
- The opportunity to work on high-impact machine learning products used in real-world financial and operational decisions.
- Close collaboration with experienced Data Science, Engineering, Product, and MLOps teams.
- Opportunities for professional development and career growth.
- Competitive compensation and benefits.

📌 Sr Data Scientist (Santiago)
🏢 Ria Money Transfer
📍 Santiago