CO - Sr. Data Scientist - 229
At a glance
Senior Data Science role at Thaloz. Remote, Brazil.
Pay not stated Data Science salary
remoteroles summary, based on the employer's posting.
What you'll do
- Design experiments such as A/B tests and causal analyses, then assess model performance with offline evaluations
- Build and refine predictive models for credit risk, revenue forecasts and policy results
- Track model health by setting success measures, investigating drift and improving data quality and feature reliability
- Work with product and engineering to move models into production with reliable, repeatable workflows
- Explain findings to technical and non-technical partners, while improving reviews, documentation and team practice
What you bring
- At least five years in data science, applied machine learning or a related quantitative field
- Strong statistics and experiment design skills, including hypothesis testing, causal reasoning and evaluation
- A record of shipping predictive models and measuring their real-world impact
- Proficiency in Python and SQL, plus experience with production data workflows
- Experience owning model performance, monitoring for drift and strengthening feature reliability
Who this fits
This hands-on senior role suits someone who can turn unclear questions into production models and measurable outcomes. You’ll work across data science, machine learning and product, and support teammates through pairing, feedback and shared practices. The role is remote in Brazil.
From the employer
Clearco is a leader in AI and data-driven eCommerce funding, providing non-dilutive capital that helps founders grow without sacrificing equity. We are hiring a Senior Data Scientist to shape the models, experiments, and analytics that drive our risk, underwriting, and revenue decisions.
This hands-on senior role sits at the intersection of Data Science, Machine Learning, and Product. You will partner with Engineering, Product, Risk, and Finance to turn ambiguous problems into production-grade models and measurable outcomes that responsibly scale funding for eCommerce businesses.
Responsibilities
- Design and execute data science experiments, including causal analysis, A/B tests, and offline evaluation.
- Develop, evaluate, and iterate on predictive models for credit/risk scoring, revenue forecasting, and policy performance.
- Own model performance and monitoring: define success metrics, investigate drift, and drive improvements to data quality and feature reliability.
- Partner with Product Engineering to productionize models and analytics with emphasis on reliability, reproducibility, and maintainability.
- Perform exploratory data analysis, feature engineering, and robust validation on real-world, messy data.
- Communicate insights and recommendations clearly to technical and non-technical stakeholders through documentation and presentations.
- Improve analytical standards, code review practices, and documentation to raise technical quality.
- Mentor and support team members through pairing, feedback, and sharing best practices.
Requirements
- 5+ years of professional experience in data science, applied machine learning, or a related quantitative role.
- Strong foundations in statistics and experimentation, including hypothesis testing, causal reasoning, and evaluation design.
- Proven experience building and shipping predictive models (classification, regression, time series) and measuring real-world impact.
- Strong proficiency in Python and SQL and comfort working with production data workflows.
- Experience defining success metrics, aligning with stakeholders, and delivering end-to-end outcomes.
- Strong written communication skills and a pragmatic approach to fast-moving environments.
- Experience owning model performance, monitoring for drift, and improving feature reliability.
Nice to Have
- Experience with credit risk, underwriting, fraud/risk signals, or financial forecasting.
- Experience with modern data tooling and warehouses such as BigQuery or Snowflake and transformation frameworks like dbt.
- Familiarity with MLOps patterns (model deployment, monitoring, feature stores, orchestration) and cloud environments.
- Experience working with messy third-party data sources (banking data, eCommerce platforms, marketing signals).
Data Science work from LinkedIn, X, Reddit and Upwork, in one list.
Each morning you get everything that fits, strongest first.
Data ScienceRemote
Find me workBy the team behind remoteroles.