Head Of Machine Learning And Data Science

Description:

Key responsibilities

Develop and execute a comprehensive strategy for the ML and Data Science Center of Excellence, aligning with the company's overall business objectives and tech enablement goals.

- Build and lead a high-performing, cross-functional team of ML engineers, data scientists, and domain experts, fostering a collaborative and innovative culture.

- Evaluate and select the appropriate ML models, frameworks, and technologies (e.g., TensorFlow, PyTorch, Kafka) to address specific business challenges and drive automation across various service lines.

- Collaborate closely with stakeholders, including the CTO, CEO, SVP of Legal Services, Product Management, and Development teams, to identify opportunities for ML and LLM integration and drive impactful projects.

- Ensure data security, privacy, and compliance with relevant regulations (e.g., GDPR, EU AI legislation) by designing robust data governance frameworks and embedding ethical AI principles into all solutions.

- Oversee the end-to-end development and deployment of ML and LLM solutions, from ideation and prototyping to production rollout and continuous improvement.

- Establish best practices, standards, and processes for ML model development, validation, and production deployment, leveraging cloud infrastructure (AWS, Azure) and MLOps principles.

- Develop and implement robust performance metrics and KPIs to measure the success and impact of ML and LLM initiatives, ensuring alignment with business objectives and ROI targets.

- Collaborate with external partners, research institutions, and industry experts to stay abreast of emerging trends, technologies, and best practices in the field of ML and AI.

The successful applicant will have a set of the following skills

- Proven expertise in machine learning, deep learning, and natural language processing (NLP) techniques, with a deep understanding of algorithms, architectures, and model optimization.

- Hands-on experience in building and deploying end-to-end ML solutions, from data ingestion and preprocessing to model training, evaluation, and productionization.

- Strong programming skills in Python, with proficiency in ML frameworks (e.g., TensorFlow, PyTorch) and data engineering tools (e.g., Kafka, Spark).

- Expertise in cloud computing platforms (AWS, Azure) and containerization technologies (Docker) for scalable and reliable ML model deployment.

- Solid understanding of data privacy, security, and ethical AI principles, with the ability to design and implement robust data governance frameworks.

- Excellent communication and stakeholder management skills, with the ability to effectively collaborate with cross-functional teams and translate technical concepts into business terms.

- Strong leadership and people management capabilities, with the ability to attract, develop, and retain top talent in the field of ML and data science.

- Strategic thinking and problem-solving skills, with the ability to identify opportunities, develop innovative solutions, and drive business impact.