Data Scientist – Onsite | Texas

Key Responsibilities

Lead the design, development, and deployment of predictive and prescriptive models to drive business impact across multiple domains.
Apply causal inference and advanced statistical techniques (e.g., propensity score matching, A/B testing, structural equation modeling, synthetic controls) to uncover cause–effect relationships and guide data-driven decisions.
Build and operationalize NLP solutions for unstructured text data, including entity extraction, sentiment analysis, classification, and topic modeling.
Develop, optimize, and maintain large-scale data pipelines and analytics workflows using Azure and Databricks.
Partner with engineering, product, and business teams to translate complex business problems into scalable data science solutions.
Deliver clear and actionable insights through visualizations, reports, and presentations tailored to both technical and executive audiences.
Champion best practices in model development, deployment, versioning, and monitoring across the data science lifecycle.

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).
Proven experience with causal analysis (experiments, quasi-experiments, or causal inference frameworks).
Proficiency in Natural Language Processing (NLP) using modern libraries such as HuggingFace, Spark NLP, or spaCy.
Advanced programming skills in Python (pandas, scikit-learn, statsmodels, PySpark) and SQL.
Hands-on experience with Databricks for large-scale data engineering and machine learning workflows.
Strong knowledge of Azure Cloud Services (Azure ML, Azure Data Lake, Fabric, Azure SQL, Azure Functions).
Understanding of MLOps principles (model versioning, CI/CD for ML, monitoring, reproducibility).
Excellent communication and storytelling skills, with the ability to present insights effectively to diverse audiences.

Preferred Qualifications

Advanced degree (MS or PhD) in Data Science, Computer Science, Statistics, Applied Mathematics, or a related field.
Experience with deep learning frameworks (TensorFlow, PyTorch) for NLP or complex modeling tasks.
Exposure to healthcare, life sciences, or other regulated industries emphasizing interpretability and causal analysis.
Familiarity with reinforcement learning, prescriptive optimization, or advanced decision science methodologies.
Contributions to open-source projects, academic publications, or recognized thought leadership in the data science community.

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