Lead Data Scientist

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.

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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.

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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.

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