Durst Kerridge

Data Scientist

Durst Kerridge

full-time

Posted on:

Location Type: Remote

Location: Anywhere in North America

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Job Level

Junior

Tech Stack

AirflowAWSAzureCloudGoogle Cloud PlatformPythonSQL

About the role

  • Predictive modeling & decision support
  • Develop and validate response, premium, and other predictive models to improve targeting and profitability for direct mail and online campaigns.
  • Build omnichannel optimization and personalization models to determine who should receive which offer/message, through which channel, and when, to maximize campaign performance and profitability.
  • Analyze historical customer and marketing data to identify trends and patterns that inform strategy and optimization.
  • Design tests/experiments to evaluate campaign parameters (e.g., pricing, coverage, response, creative) and quantify lift and impact.
  • Production-ready analytics & ML
  • Build new and upgrade existing pipelines for data flow, business processes, scoring, and reporting (batch-first, designed for scale).
  • Contribute to model delivery patterns such as scheduled scoring jobs, APIs, or containerized services, including lightweight monitoring and versioning.

Requirements

  • MS or PhD in a quantitative field (e.g., Data Science, Computer Science, Mathematics, Statistics, or related field); regardless of degree, demonstrated experience delivering predictive models in production-like environments.
  • At least 1-3 years of experience.
  • Strong experience in Python and/or R for analysis, modeling, and building repeatable workflows (Python preferred for production use cases).
  • Demonstrated ability to build predictive models using statistical and machine learning techniques.
  • Strong SQL and experience working with warehouse-style data structures and/or analytics datasets.
  • Ability to work effectively with ambiguous requirements and real-world data quality constraints while still delivering dependable results.
  • Experience with cloud computing (AWS/Azure/GCP) for data processing or model deployment.
  • Solid communication skills—able to explain assumptions, tradeoffs, and results to both technical and non-technical audiences.
  • Familiarity with orchestration / pipelines (e.g., Airflow/Prefect/Dagster) and CI/CD practices for data or ML workflows preferred.
  • Exposure to model lifecycle management concepts (e.g., model versioning, basic monitoring, and maintaining reproducible training/scoring workflows) preferred.
Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools
predictive modelingdecision supportdata analysisstatistical techniquesmachine learningPythonRSQLcloud computingmodel lifecycle management
Soft Skills
communication skillsability to work with ambiguous requirementsdependable results