Overview
Remote
Depends on Experience
Contract - W2
Contract - 6 Month(s)
Skills
Databricks
Machine Learning Operations (ML Ops)
Microsoft Azure
PySpark
scikit-learn
Amazon Web Services
Docker
Kubernetes
Modeling
Python
SQL
Job Details
Experience:
- 3+ years of hands-on experience in customer analytics, segmentation, or predictive modeling within a commercial, marketing, or customer-focused environment.
- Proven track record of delivering analytics that drive business decisions and measurable outcomes.
Technical Skills:
- Advanced proficiency in Python (pandas, scikit-learn, PySpark, SQL functions) and experience with Spark for large-scale data processing.
- Demonstrated experience with clustering, propensity modeling, uplift modeling, and customer value analysis.
- Strong background in feature engineering, data enrichment, and data quality management.
- Experience with MLOps tools and practices (e.g., MLflow, Kubeflow, Airflow, Docker, Kubernetes) for model deployment, monitoring, and lifecycle management.
- Proficiency with version control (Git) and CI/CD pipelines for automating analytics workflows.
- Experience deploying models and analytics solutions to cloud platforms (Azure, AWS, Google Cloud Platform) and monitoring their performance in production.
Business & Communication Skills:
- Ability to translate complex analytics into clear, actionable insights for commercial and marketing stakeholders.
- Experience working cross-functionally with business teams to identify needs, deliver solutions, and drive adoption.
- Excellent written and verbal communication skills, including documentation and training for non-technical users.
- Strong problem-solving skills, business curiosity, and a results-driven mindset.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.