If you require a green card either now or in the future, we will not be sponsoring anyone for green cards.
This is a full-time remote position (working from anywhere in the continental USA only).
Position Summary
We are seeking a Data Scientist to design, develop, and deploy B2B propensity models that improve customer engagement, marketing effectiveness, and business decision-making. This role will leverage advanced statistical modeling and machine learning techniques to predict customer behaviors such as purchase propensity, churn, cross-sell opportunities, and response to marketing campaigns.
The ideal candidate combines deep analytical expertise with strong business acumen and can translate complex modeling results into actionable strategies that drive measurable business outcomes.
Key Responsibilities
- Design, build, validate, and maintain propensity models for customer acquisition, retention, cross-sell, upsell, and campaign response.
- Develop predictive models using statistical and machine learning techniques including logistic regression, gradient boosting, random forests, and other classification methods.
- Engineer features from large, complex datasets to improve model performance.
- Evaluate model performance using appropriate metrics such as AUC, lift, precision/recall, calibration, and business impact.
- Partner with Marketing, Product, CRM, and Business stakeholders to identify high-value modeling opportunities.
- Translate analytical findings into clear recommendations for technical and non-technical audiences.
- Monitor deployed models for drift, degradation, and ongoing performance.
- Support experimentation through A/B testing, champion/challenger models, and continuous model improvement.
- Document methodologies, assumptions, and validation results to ensure model transparency and governance.
- Collaborate with Data Engineering and Machine Learning Engineering teams to productionize models.
Required Qualifications
- Bachelor's degree in Statistics, Mathematics, Computer Science, Data Science, quantitative discipline (Master's preferred).
- 2+ years of experience developing predictive or propensity models in a commercial environment.
- Strong understanding of statistical modeling and machine learning techniques.
- Advanced proficiency in Python and SQL.
- Experience with machine learning libraries such as scikit-learn, XGBoost, LightGBM, or CatBoost.
- Experience working with large datasets in cloud or distributed environments.
- Strong communication and stakeholder management skills.
Preferred Experience
- Experience in customer analytics, marketing analytics, financial services, healthcare, insurance, telecommunications, or retail.
- Experience with uplift modeling, causal inference, or next-best-action modeling.
- Familiarity with cloud platforms such as AWS, Azure, or Google Cloud.
- Experience deploying models into production using MLOps best practices.
- Knowledge of customer lifetime value (CLV) modeling and segmentation techniques.
Technical Skills
- Python
- SQL
- R (preferred)
- scikit-learn
- XGBoost / LightGBM / CatBoost
- Git
- Jupyter Notebooks
- Spark (preferred)
- Tableau or Power BI (preferred)
We are an E-verify company.
Job Type: Full-time
Pay: $100,000.00 - $140,000.00 per year
Benefits:
Application Question(s):
- How many years of Datorama or Tableau experience do you have?
- Is your degree in one of the following fields: math, stats, computer science, data science or statistics?
Education:
Experience:
- SQL: 2 years (Required)
- python: 2 years (Required)
- Data Science/Analyst: 2 years (Required)
- Machine learning: 2 years (Required)
- digital ad platforms: 2 years (Required)
- propensity modeling: 2 years (Required)
Language:
- and write English fluently (Required)
Work Location: Remote
