Lead Data Scientist (Python & SQL) - Malvern, PA USA

Role: Lead Data Scientist (Python & SQL)

Location: Malvern, PA - Onsite Opportunity

Experience: 10/12+ Years

Job Description:

The Lead Data Scientist will be responsible for guiding the design and execution of advanced analytics and machine learning solutions that directly support strategic business initiatives. This role will work closely with product, engineering, and business stakeholders to translate complex problems into scalable data science frameworks, while ensuring high standards in code quality, experimentation, and data integrity.

Responsibilities:

• Lead the end-to-end development of predictive models, optimization algorithms, and analytical frameworks using Python and SQL.
• Partner with product and business teams to define problem statements, analytical approaches, and measurable success criteria.
• Build and maintain robust data pipelines for feature engineering, model training, and model deployment in collaboration with data engineering teams.
• Conduct in-depth exploratory data analysis to uncover insights, identify opportunities, and validate business hypotheses.
• Oversee model validation, versioning, monitoring, and performance optimization in production environments.
• Establish best practices for statistical modeling, experimentation design (A/B testing), and data science documentation.
• Work with engineering teams to integrate machine learning models into applications, workflows, and automated decision engines.
• Provide technical leadership and mentorship to data scientists and analysts, ensuring consistency in methodology and coding practices.
• Develop dashboards, automated reports, and visualization tools to communicate insights and model outcomes to senior stakeholders.
• Translate analytical findings into actionable recommendations that support product strategy, operational improvements, and customer experience initiatives.
• Ensure adherence to data governance, privacy regulations, and responsible AI practices across all modeling activities.
• Evaluate emerging data science tools, libraries, and platforms to continuously improve team efficiency and solution quality.
• Manage project timelines, resource planning, and stakeholder updates across multiple parallel data science initiatives.