Data Scientist_only on W2

Required Skills : Must have Databricks experience Education • 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 • 6+ 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, CPG, 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.