Job Title: Data ScientistLocation: RemoteContract: 6+ MonthsOnly W2(USC/GC)Job Description:In this pivotal role, you'll enable targeted marketing, customer engagement, and data-driven commercial strategies through robust segmentation, predictive modeling, and campaign analytics. You'll also play a key role in deploying, automating, and maintaining production-grade analytics solutions using modern MLOps practices.What You'll Do
Customer Segmentation:
Build and maintain flexible, multi-level customer segmentation frameworks using clustering and advanced feature engineering, enabling differentiated marketing and sales strategies.
Propensity and Predictive Modeling:
Develop models to predict customer behaviors such as likelihood to purchase, respond to campaigns, or churn, empowering teams to focus resources where they'll have the greatest impact.
Customer Value Analysis:
Estimate and analyze customer lifetime value (CLV) and identify high-potential and at-risk customers to inform retention and growth initiatives.
Campaign Measurement & Uplift Modeling:
Evaluate the effectiveness of marketing campaigns and interventions, using uplift modeling and other techniques to optimize spend and strategy.
Next-Best-Action/Product Recommendations:
Deliver insights and tools that recommend the most relevant product, service, or engagement for each customer segment.
Model Deployment & Productionization:
Deploy predictive models and analytics solutions into production environments, ensuring reliability, scalability, and maintainability.
Automation & Monitoring:
Build and maintain automated pipelines for model training, deployment, and monitoring, enabling continuous improvement and reliability of analytics solutions.
Code Review & Collaboration:
Participate in code reviews (PRs) and collaborate with engineering teams to ensure code quality, reproducibility, and adherence to best practices.
Stakeholder Engagement:
Partner with commercial, marketing, and product teams to identify analytics needs, deliver impactful solutions, and provide training and documentation for end users.
What You'll NeedEducation:
Bachelor's degree (Master's preferred) in a quantitative field such as Econometrics, Statistics, Marketing Science, Business Analytics, Quantitative Marketing, Applied Mathematics, or a related discipline.
Master's degree or higher in any of the above fields, or equivalent professional experience demonstrating advanced technical and business analytics skills.
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, GCP) 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.
Preferred Qualifications
Experience in commercial analytics, marketing analytics, or customer analytics roles within agriculture, retail, Client, or other B2B industries with complex customer relationships.
Familiarity with causal inference in observational studies, next-best-action modeling, and customer journey analytics.
Experience building self-service analytics tools or utilities for business teams.
Knowledge of data governance best practices and experience supporting data-driven business transformation.
Knowledge of MLOps best practices for deploying and managing production models, including monitoring, versioning, and automation.
Experience with containerization (Docker, Kubernetes) and orchestration tools for scalable analytics operations.
Experience participating in code reviews and collaborative development processes.
Familiarity with building automated pipelines for model training, deployment, and monitoring.
Proficiency with PySpark for large-scale data processing.
