isolved
Summary
We are seeking a highly skilled Data Scientist to focus on building and deploying predictive models that identify customer churn risk and upsell opportunities. This role will play a key part in driving revenue growth and retention strategies by leveraging advanced machine learning, statistical modeling, and large-scale data capabilities within Databricks.
Why Join Us?
Be at the forefront of using Databricks AI/ML capabilities to solve real‑world business challenges.
Directly influence customer retention and revenue growth through applied data science.
Work in a collaborative environment where experimentation and innovation are encouraged.
Core Job Duties
Model Development
Design, develop, and deploy predictive models for customer churn and upsell propensity using Databricks ML capabilities.
Evaluate and compare algorithms (e.g., logistic regression, gradient boosting, random forest, deep learning) to optimize predictive performance.
Incorporate feature engineering pipelines that leverage customer behavior, transaction history, and product usage data.
Data Engineering & Pipeline Ownership
Build and maintain scalable data pipelines in Databricks (using PySpark, Delta Lake, and MLflow) to enable reliable model training and scoring.
Collaborate with data engineers to ensure proper data ingestion, transformation, and governance.
Experimentation & Validation
Conduct A/B tests and backtesting to validate model effectiveness.
Apply techniques for model monitoring, drift detection, and retraining in production.
Business Impact & Storytelling
Translate complex analytical outputs into clear recommendations for business stakeholders.
Partner with Product and Customer Success teams to design strategies that reduce churn, increase upsell, and improve customer retention KPIs.
Minimum Qualifications
Master's or PhD in Data Science, Statistics, Computer Science, or related field (or equivalent industry experience).
3+ years of experience building predictive models in a production environment.
Strong proficiency in Python (pandas, scikit‑learn, PySpark) and SQL.
Demonstrated expertise using Databricks for:
Data manipulation and distributed processing with PySpark.
Building and managing models with MLflow.
Leveraging Delta Lake for efficient data storage and retrieval.
Implementing scalable ML pipelines within Databricks' ML Runtime.
Experience with feature engineering for behavioral and transactional datasets.
Strong understanding of customer lifecycle analytics, including churn modeling and upsell/recommendation systems.
Ability to communicate results and influence decision‑making across technical and non‑technical teams.
Preferred Qualifications
Experience with cloud platforms (Azure Databricks, AWS, or GCP).
Familiarity with Unity Catalog for data governance and security.
Knowledge of deep learning frameworks (TensorFlow, PyTorch) within Databricks.
Exposure to MLOps best practices (CI/CD for ML, model versioning, monitoring).
Background in SaaS, subscription‑based businesses, or customer analytics.
Physical Demands Prolonged periods of sitting at a desk and working on a computer. Must be able to lift up to 15 pounds.
Travel Required Limited
Work Authorization Employees must be legally authorized to work in the United States.
FLSA Classification Exempt
Location Any
Effective Date 9/16/2025
Equal Opportunity Employer isolved is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.
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Why Join Us?
Be at the forefront of using Databricks AI/ML capabilities to solve real‑world business challenges.
Directly influence customer retention and revenue growth through applied data science.
Work in a collaborative environment where experimentation and innovation are encouraged.
Core Job Duties
Model Development
Design, develop, and deploy predictive models for customer churn and upsell propensity using Databricks ML capabilities.
Evaluate and compare algorithms (e.g., logistic regression, gradient boosting, random forest, deep learning) to optimize predictive performance.
Incorporate feature engineering pipelines that leverage customer behavior, transaction history, and product usage data.
Data Engineering & Pipeline Ownership
Build and maintain scalable data pipelines in Databricks (using PySpark, Delta Lake, and MLflow) to enable reliable model training and scoring.
Collaborate with data engineers to ensure proper data ingestion, transformation, and governance.
Experimentation & Validation
Conduct A/B tests and backtesting to validate model effectiveness.
Apply techniques for model monitoring, drift detection, and retraining in production.
Business Impact & Storytelling
Translate complex analytical outputs into clear recommendations for business stakeholders.
Partner with Product and Customer Success teams to design strategies that reduce churn, increase upsell, and improve customer retention KPIs.
Minimum Qualifications
Master's or PhD in Data Science, Statistics, Computer Science, or related field (or equivalent industry experience).
3+ years of experience building predictive models in a production environment.
Strong proficiency in Python (pandas, scikit‑learn, PySpark) and SQL.
Demonstrated expertise using Databricks for:
Data manipulation and distributed processing with PySpark.
Building and managing models with MLflow.
Leveraging Delta Lake for efficient data storage and retrieval.
Implementing scalable ML pipelines within Databricks' ML Runtime.
Experience with feature engineering for behavioral and transactional datasets.
Strong understanding of customer lifecycle analytics, including churn modeling and upsell/recommendation systems.
Ability to communicate results and influence decision‑making across technical and non‑technical teams.
Preferred Qualifications
Experience with cloud platforms (Azure Databricks, AWS, or GCP).
Familiarity with Unity Catalog for data governance and security.
Knowledge of deep learning frameworks (TensorFlow, PyTorch) within Databricks.
Exposure to MLOps best practices (CI/CD for ML, model versioning, monitoring).
Background in SaaS, subscription‑based businesses, or customer analytics.
Physical Demands Prolonged periods of sitting at a desk and working on a computer. Must be able to lift up to 15 pounds.
Travel Required Limited
Work Authorization Employees must be legally authorized to work in the United States.
FLSA Classification Exempt
Location Any
Effective Date 9/16/2025
Equal Opportunity Employer isolved is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.
#J-18808-Ljbffr