Lead Data Scientist

We are seeking a highly experienced and strategic Lead Data Scientist to drive data-driven decision-making and lead advanced analytics initiatives across the organization. The ideal candidate will combine strong technical expertise with leadership capabilities to design, develop, and deploy scalable data science solutions. This role requires a hands-on leader who can mentor a team, collaborate with stakeholders, and translate complex data into actionable insights. Key Responsibilities Lead the design, development, and deployment of machine learning models and advanced analytics solutions Define data science strategy aligned with business objectives Collaborate with cross-functional teams to identify opportunities for leveraging data Oversee data collection, preprocessing, and feature engineering processes Build and optimize predictive and prescriptive models Communicate insights and recommendations to stakeholders and executive leadership Establish best practices for data science, model governance, and experimentation Mentor and guide junior data scientists and analysts Ensure data quality, integrity, and compliance with regulatory standards Stay current with emerging trends and technologies in data science and AI

Must be currently residing in the United States Valid U.S. work authorization (citizen, permanent resident, or authorized work permit holder) Proven experience as a Data Scientist with leadership or team management responsibilities Strong proficiency in Python or R for data analysis and machine learning Experience with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) Solid understanding of statistics, probability, and data modeling Experience working with large datasets and distributed computing tools (e.g., Spark) Strong knowledge of SQL and data manipulation techniques Excellent problem-solving, analytical, and communication skills Preferred Qualifications Advanced degree (Master's or PhD) in Data Science, Computer Science, Statistics, or a related field Experience with cloud platforms (AWS, Azure, or Google Cloud) Familiarity with MLOps practices and tools Experience in building data pipelines and data engineering workflows Knowledge of data visualization tools (Tableau, Power BI)

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