Lead Data Scientist (US Healthcare)

Accountabilities:

The Lead Data Scientist will own the data science strategy and help translate advanced analytics into scalable product capabilities, while leading a growing team of data science professionals.

• Define and execute the data science strategy in alignment with product goals and business priorities.
• Lead data science initiatives that support the development and evolution of AI/ML-powered B2B SaaS products.
• Solve complex business challenges by combining traditional statistical and mathematical modeling with modern AI, machine learning, and deep learning techniques.
• Develop and apply advanced analytical approaches to generate meaningful insights from broad and complex datasets.
• Transform complex or black-box AI models into more transparent and interpretable solutions using explainable AI techniques.
• Work hands-on with data scientists, software engineers, and other technical teams to turn analytical concepts into scalable product solutions.
• Promote a culture of creative thinking, experimentation, ownership, and practical problem-solving across the data science organization.
• Hire, mentor, guide, and coach Data Science team members while developing the capabilities required to support rapid organizational growth.
• Establish effective approaches, standards, and practices for data science, modeling, experimentation, and delivery.
• Help democratize advanced analytics by enabling business users to leverage data and analytical insights to address complex business challenges.
• Contribute to thought leadership through whitepapers, articles, conferences, and other industry-facing initiatives.
• Stay current with emerging AI/ML methodologies and identify opportunities to incorporate relevant innovations into products and solutions.

Requirements:

The ideal candidate combines strong hands-on data science expertise with the ability to lead teams, structure complex problems, and translate advanced analytical techniques into tangible business outcomes.

• Master’s degree in Machine Learning, Mathematics, Computer Science, Statistics, Economics, or a related quantitative field.
• 6+ years of hands-on experience developing and applying data-driven solutions in a corporate or consulting environment.
• Experience working in or with CPG, Retail, Healthcare, or other data-intensive industries is preferred.
• Strong understanding of statistical and machine learning techniques, including regression, boosting, segmentation, Marketing Mix Modeling (MMM), and deep learning.
• Demonstrated ability to solve complex data science problems using both traditional analytical methods and advanced AI/ML/DL techniques.
• Hands-on experience with R, Python, Spark, TensorFlow, Caffe, and PyTorch.
• Experience working with big data platforms and technologies such as Apache Spark and Hadoop.
• Strong knowledge of data engineering, data modeling, algorithms, and modern data science practices.
• Ability to translate complex analytical concepts into practical business solutions and measurable outcomes.
• Strong problem-solving skills with the ability to structure, prioritize, and execute analytical approaches based on potential business impact.
• Intellectual curiosity and a continuous interest in emerging technologies and analytical methodologies.
• Demonstrated ability to lead, motivate, and develop high-performing data science teams.
• A hands-on, execution-oriented mindset with the ability to move from strategy and ideas to implementation.
• Strong communication and collaboration skills when working with technical and business stakeholders.

Benefits:

• Remote work opportunity based in Karnataka, India.
• Opportunity to lead data science strategy for AI/ML-powered B2B SaaS products.
• Exposure to complex business challenges across the healthcare and CPG industries.
• Strong combination of strategic leadership and hands-on technical work.
• Opportunity to build and scale a Data Science team as the organization grows.
• Collaboration with experienced data scientists, engineers, and multidisciplinary teams.
• Opportunity to work with advanced AI, machine learning, deep learning, and explainable AI technologies.
• Opportunities to contribute to thought leadership through whitepapers, industry articles, and conferences.
• High level of ownership and influence over data science initiatives and product development.