Entry Level Data Scientist – Collections Analytics
Segoso
full-time
Posted on:
Location Type: Remote
Location: United States
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Job Level
About the role
- Report directly to the Vice President of Strategic Initiatives and support analytics and modeling projects across account segmentation, contact strategy optimization, and early-warning detection.
- Work with cross-functional teams (operations, compliance, IT) to turn data into actionable insights, build repeatable analytics, and productionize models.
- Build, validate, and maintain predictive models (e.g., credit/payment propensity, churn/risk, contact response) using Python/R and standard ML libraries.
- Clean, explore, and feature-engineer large-structured datasets from multiple sources (payments, contact history, credit bureau, CRM).
- Run A/B tests and propensity-matched experiments to evaluate contact strategies and messaging, measure lift and report results.
- Create dashboards, visualizations, and regular reports for operations and leadership (KPIs: recovery rates, promise-to-pay keep rates, contact conversion, roll rates).
- Assist in productionizing models: packaging, documentation, monitoring, and retraining schedules in collaboration with others.
- Implement and track model performance, fairness, and stability metrics; support model governance and compliance documentation.
- Support data pipelines and ETL tasks; partner with data engineering to ensure data quality and lineage.
- Translate business problems into analytical approaches; present findings and recommended actions to stakeholders.
- Stay current on best practices in supervised/unsupervised learning, causal inference, and responsible AI as applied to collections.
Requirements
- Bachelor’s degree (or equivalent) in Data Science, Statistics, Computer Science, Mathematics, Industrial Engineering, or related quantitative field; graduation within the last 2-3 years.
- Solid SQL skills for data extraction and manipulation.
- Strong programming skills in Python.
- Understanding of basic ML algorithms (logistic regression, decision trees, ensemble methods, clustering) and evaluation metrics (precision/recall, confusion matrix, calibration).
- Experience with data visualization tools (Power BI, Tableau or equivalent) or ability to produce clear visual analyses in Python/R.
- Hands-on experience with at least one machine learning project (class project, internship, capstone, or competition) with end-to-end work (data cleaning, modeling, evaluation).
- Strong analytical problem-solving, attention to detail, and ability to communicate technical results to non-technical stakeholders.
- Commitment to ethical data practices and regulatory compliance (willingness to learn collections/consumer protection rules).
Benefits
- Flexible work arrangements
- Professional development
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
PythonRSQLmachine learningpredictive modelingdata cleaningfeature engineeringA/B testingdata visualizationETL
Soft Skills
analytical problem-solvingattention to detailcommunicationcollaborationstakeholder engagementadaptabilitycritical thinkingtime managementpresentation skillsethical data practices