Modelling & Data Science Director

Job Description:

Key Responsibilities

Team Leadership & Management

Lead and manage a team of 5-10 AI engineers, ML engineers, and data scientists

Conduct performance reviews, provide mentoring, and support career development

Foster a collaborative culture focused on innovation and continuous learning

Manage team workload distribution and resource allocation

Technical Leadership

Design and develop AI models and algorithms from scratch. Implement AI solutions that integrate with existing business systems to enhance functionality and user interaction

Provide technical guidance on AI architecture, model selection, and implementation strategies

Review code, ensure best practices, and maintain high technical standards

Drive technical decision-making for AI platform architecture and tooling

Stay current with AI/ML trends and evaluate new technologies for adoption

Project Management & Delivery

Plan, execute, and deliver AI projects from conception to production deployment

Coordinate cross-functional initiatives with product, engineering, and business teams

Manage project timelines, budgets, and resource requirements

Ensure AI solutions meet scalability, performance, and reliability standards

Implement and maintain CI/CD pipelines for ML model deployment

Strategic Planning & Innovation

Develop AI engineering roadmaps aligned with business objectives

Identify opportunities to leverage AI for business value and competitive advantage

Establish engineering best practices, coding standards, and quality assurance processes

Drive innovation initiatives and research into emerging AI technologies

Contribute to AI strategy discussions with senior leadership

Required Technical Skills

AI/ML Expertise

Python

Strong background in machine learning algorithms, deep learning, and neural networks

Experience with ML frameworks: TensorFlow, PyTorch, scikit-learn, Keras

Knowledge of computer vision, NLP, and statistical modeling techniques

Data Engineering & Infrastructure

Experience with data pipeline development and ETL processes

Knowledge of big data technologies (Spark, Hadoop, Kafka)

Understanding of database systems (SQL, NoSQL, vector databases)

Data preprocessing, feature engineering, and data quality management

Cloud & DevOps

Experience with cloud platforms (AWS, Azure, GCP) for AI/ML workloads

MLOps tools and practices for model lifecycle management

Containerization and orchestration (Docker, Kubernetes)

Continuous Deployment (CD)

Experience Requirements

8+ years of AI/ML engineering experience with hands-on model development and deployment

3+ years of team leadership or technical management experience

Proven track record of delivering production AI systems at scale

Experience managing the complete ML lifecycle from research to production

Background in both individual contribution and team collaboration

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