Sr. Data Scientist
OnPoint Insights
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Raleigh-Durham-Chapel Hill Area
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Associate
Posted March 13, 2026
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Responsibilities
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.
Not Met Priorities
What still needs stronger evidence
Requirements
- 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 Skills
- 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
Education
- (Not required) – Advanced degree (MS or PhD) in Data Science, Computer Science, Statistics, Applied Mathematics, or related field .
- (Not required) – Exposure to healthcare, life sciences, or other regulated industries where causal analysis and interpretability are critical.
- (Not required) – Familiarity with reinforcement learning, prescriptive optimization, or advanced decision sciences .
- (Not required) – Contributions to open-source projects, publications, or thought leadership in the data science community
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
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