Candidates local in North Carolina will be preferred.
Key Responsibilities
- Lead the development and deployment of predictive and prescriptive models to optimize business outcomes across multiple domains.
- Apply causal inference and statistical analysis techniques (e.g., propensity score matching, A/B testing, structural equation modeling, synthetic controls) to uncover cause–effect relationships and support decision-making.
- Develop and operationalize NLP solutions for unstructured text data, including entity extraction, text classification, sentiment analysis, and topic modeling.
- Build, optimize, and maintain large-scale data pipelines and analytical workflows in Azure and Databricks environments.
- Collaborate with cross-functional teams (engineering, product, business stakeholders) to translate business problems into data science solutions.
- Communicate insights and recommendations clearly through visualizations, reports, and presentations to technical and non-technical audiences.
- Contribute to building best practices in model development, deployment, and monitoring.
Required Qualifications
- 5+ years of professional experience in Data Science or Advanced Analytics.
- Strong expertise in predictive modeling, prescriptive analytics, and statistical methods (regression, classification, clustering, optimization).
- Hands-on experience with causal analysis (e.g., causal inference frameworks, experiments, quasi-experiments).
- Proficiency in Natural Language Processing (NLP) using modern libraries (e.g., HuggingFace, Spark NLP, spaCy).
- Proficient in Python (pandas, scikit-learn, statsmodels, PySpark) and SQL.
- Advanced knowledge of Databricks for large-scale data engineering and machine learning workflows.
- Strong experience with Azure Cloud Services (e.g., Azure Machine Learning, Azure Data Lake, Fabric, Azure SQL, Functions).
- Solid understanding of MLOps practices (versioning, CI/CD for ML, monitoring, reproducibility).
- Excellent communication skills with ability to present findings to both technical and executive stakeholders.
Preferred Qualifications
- Advanced degree (MS or PhD) in Data Science, Computer Science, Statistics, Applied Mathematics, or related field.
- Experience with deep learning frameworks (TensorFlow, PyTorch) for NLP and other advanced modeling tasks.
- Exposure to healthcare, life sciences, or other regulated industries where causal analysis and interpretability are critical.
- Familiarity with reinforcement learning, prescriptive optimization, or advanced decision sciences.
- Contributions to open-source projects, publications, or thought leadership in the data science community.
