Role Overview :

As a Machine Learning Lead, you will spearhead the design and deployment of sophisticated AI solutions that drive strategic decision-making and product innovation. You will operate at the intersection of data science and engineering, collaborating closely with cross-functional product teams, data engineers, and executive stakeholders to translate complex business challenges into scalable algorithmic models. Your work will directly influence the end-user experience by optimizing core platform features and automating high-impact workflows, ensuring our technology remains at the cutting edge of the industry. This role is pivotal in shaping our technical roadmap and mentoring a high-performing team of data scientists to achieve operational excellence.

Key Responsibilities :

- Architect and oversee the end-to-end lifecycle of machine learning models, from initial data exploration to production deployment, to ensure high-performance outcomes for our core business units.

- Lead the implementation of MLOps best practices to streamline model monitoring, versioning, and CI/CD pipelines, significantly reducing time-to-market for new features.

- Collaborate with product managers and business leaders to identify high-value opportunities for AI integration, ensuring that technical initiatives align with long-term company objectives.

- Mentor junior data scientists and engineers by conducting code reviews and architectural deep dives, fostering a culture of technical rigor and continuous learning.

- Optimize data modeling and big data processing workflows to handle massive datasets, improving the accuracy and latency of our predictive systems.

Required Skillset :

- Demonstrated expertise in building and deploying production-grade models using Python, TensorFlow, and PyTorch, with a deep understanding of underlying mathematical and statistical principles.

- Proven ability to design and implement advanced solutions in NLP and Computer Vision, translating research-level concepts into tangible business applications.

- Strong proficiency in cloud-based infrastructure, specifically AWS, with the ability to manage scalable environments for distributed computing and model training.

- Exceptional communication skills, with the ability to articulate complex technical concepts to non-technical stakeholders and influence strategic decision-making.

- A Masters degree or PhD in Computer Science, Statistics, Mathematics, or a related quantitative field, reflecting a strong foundation in algorithmic theory.

- Ability to thrive in a fast-paced, hybrid work environment, demonstrating the adaptability to lead remote teams while maintaining high standards of collaboration and project delivery.

- Minimum of 5 - 10 years of hands-on experience in data science and machine learning, with a track record of delivering scalable AI products in a professional setting.