Senior Data Scientist

Job not on LinkedIn

🕒 May 6

Apply Now

Find Similar Remote Jobs

Receive Emails For Remote Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

Thaloz

WebsiteLinkedInAll Job Openings

51 - 200 employees

☁️ SaaS

🤝 B2B

🏢 Enterprise

SaaS • B2B • Enterprise

Thaloz is a tech consulting and outsourcing company focused on cloud-native product development and engineering team outsourcing. They offer tailored software development solutions, leveraging top-tier talent from Latin America. Thaloz provides a variety of services such as custom product development, staff augmentation, and enterprise solutions. Their approach emphasizes people-focused consulting and remote work culture, ensuring seamless integration of skilled IT professionals into their clients' projects. With expertise in diverse technologies, Thaloz offers end-to-end support from strategy to launch, fostering smooth communication and collaboration.

📋 Description

• 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).