Lead Data Scientist

Lead and mentor a team of strong Machine Learning Engineers, synthesizing their skills and ideas into scalable, impactful solutions.

Have a proven track record in successfully leading teams in the development, deployment, and scaling of machine learning models to solve real-world business problems.

Provide leadership in structuring, analyzing, and interpreting data, delivering insights to the business side to support informed decision-making.

Oversee the full lifecycle of machine learning models - from development and training to deployment and monitoring in production environments.

Collaborate closely with cross-functional teams to align AI efforts with business goals and ensure measurable impacts.

Leverage deep expertise in machine learning, data science, or related fields to enhance document interpretation tasks such as text extraction, classification, and semantic analysis.

Utilize natural language processing (NLP) to improve our AI-based Intelligent Document Processing platform.

Implement MLOps best practices, ensuring smooth deployment, monitoring, and maintenance of models in production.

Continuously explore and experiment with new technologies and techniques to push the boundaries of AI solutions within the organization.

Lead by example, fostering a culture of innovation and collaboration within the team while effectively communicating technical insights to both technical and non-technical stakeholders.

Proven experience as a Lead Machine Learning Engineer, Data Scientist, or a similar role, with extensive leadership experience managing and mentoring engineering teams.

bachelors or masters degree in Computer Science, Data Science, Mathematics, or a related field.

Demonstrated success in leading teams that have designed, deployed, and scaled machine learning models in production environments, delivering tangible business results.

Expertise in machine learning algorithms, deep learning, and data mining techniques, ideally in an enterprise setting.

Hands-on experience with natural language processing (NLP) and data extraction tools.

Proficiency in Python (or other programming languages), and with libraries such as Scikit-learn, TensorFlow, PyTorch, etc

Strong experience with MLOps processes, including model development, deployment, and monitoring at scale.

Strong leadership and analytical skills, capable of structuring and interpreting data to provide meaningful business insights and guide decisions.

A practical understanding of aligning machine learning initiatives with business objectives.

Industry experience in insurance is a plus but not required.