Data Scientist

Position: Data Scientist

Department: MIS / Analytics

Position Type: Full Time, Exempt

Location: Remote, based in the US

Responsibilities:

• Develop predictive and prescriptive models used to forecast collections and support portfolio acquisition and business strategy decisions.
• Deploy, monitor, or maintain predictive or prescriptive models in production environments.
• Support analysis and models for portfolio acquisition opportunities.
• Apply statistical and machine learning techniques to improve hybrid forecasts.
• Design, build, and maintain robust data pipelines, transformations, and analytical workflows using SQL, Python and/or R.
• Contribute to scalable data models and schemas to support portfolio valuation, forecasting, reporting, and operational analytics.
• Implement data quality rules, validation logic, automated QA checks, and monitoring frameworks to ensure data integrity.
• Perform exploratory data analysis to identify trends, data gaps, and metric behavior related to portfolio performance.
• Translate business questions and acquisition strategy into technical requirements, datasets, and analytical solutions.
• Create reports, dashboards, and analytical outputs that are usable and interpretable by non-technical stakeholders.
• Collaborate closely with analytics, engineering, and business partners to align data definitions, metrics, and logic.
• Document data pipelines, models, transformations, business rules, and data dictionaries to support governance and continuity.

Qualifications:

• Minimum of 5 years of experience in data science or data analytics roles.
• Advanced Excel and SQL skills with hands-on experience building scalable transformations and data models.
• Proficiency in Python and/or R for analytics, automation, and modeling.
• Experience developing forecasting, valuation, pricing, or portfolio performance models.
• Experience with BI and visualization tools such as Power BI, Tableau, or Looker.
• Strong analytical thinking with the ability to translate complex data into actionable business insights.
• Ability to work effectively in ambiguous, open-ended problem spaces.
• Strong written and verbal communication skills with both technical and non-technical audiences.
• Strong understanding of statistical concepts, predictive modeling techniques, machine learning workflows, model validation methodologies, and model performance evaluation.
• Preferred Qualifications
• Familiarity with Git or version control systems.
• Background in financial services, portfolio analytics, forecasting, or operations analytics.
• Strong background in predictive, descriptive, and prescriptive modeling techniques.
• Understanding tree-based and survival models.

Core Competencies: