Job Overview
We are seeking a dynamic and industry-connected Data Science Lecturer to join our faculty. As the demand for data-driven decision-making continues to explode, you will be responsible for preparing students to navigate the complex intersection of statistics, computer science, and domain expertise. Your role is to translate abstract theory into practical, high-impact skills, equipping graduates to tackle real-world problems using machine learning, big data, and advanced analytics.

Job Responsibilities
Instructional Delivery: Deliver engaging lectures and intensive coding workshops on key data science pillars, including Machine Learning, Deep Learning, Data Visualization, Big Data Engineering, and Natural Language Processing.

Curriculum Innovation: Design and regularly refresh course content to keep pace with rapid industry shifts, incorporating modern frameworks and platforms (e.g., PyTorch, TensorFlow, Hugging Face, Spark, Cloud APIs).

Practical Lab Facilitation: Lead hands-on lab sessions where students work with real-world datasets, focusing on the end-to-end data pipeline: from data cleaning and feature engineering to model deployment.

Project Mentorship: Advise students on capstone projects and research papers, encouraging them to bridge academic inquiry with industry-relevant challenges.

Assessment & Evaluation: Create robust assessments—including coding challenges, hackathons, and research projects—that test both theoretical understanding and technical implementation.

Industry & Academic Engagement: Maintain an active research agenda or industry consultancy to ensure the curriculum remains at the cutting edge. Participate in departmental initiatives and collaborative research.

Professional Development: Host seminars or webinars on data ethics, AI safety, and the societal implications of algorithmic decision-making.

Job Requirements
Education: Master’s degree in Data Science, Computer Science, Statistics, or a related field; a PhD is highly preferred.

Experience: 3+ years of experience in data science roles within the industry or equivalent experience in tertiary teaching/research.

Technical Skills:

Programming: Mastery of Python (essential), with experience in R or C++ as a plus.

ML/AI: Proficient in scikit-learn, TensorFlow, or PyTorch, with a deep understanding of model evaluation and tuning.

Big Data/Cloud: Experience with tools like SQL, Spark, Docker, and major cloud platforms (AWS, Azure, or GCP).

Soft Skills:

Communication: Ability to explain complex algorithms and data structures clearly to students from diverse technical backgrounds.

Problem-Solving: A "hacker" mindset—creative, persistent, and methodical when debugging or tackling messy data problems.

Adaptability: A deep-seated passion for continuous learning in a field that changes almost weekly.

Inclusive Leadership: Capacity to foster a collaborative and diverse environment where all students feel empowered to innovate.

Benefits
Competitive Compensation: Attractive salary commensurate with industry experience and academic credentials.

Research & Innovation: Access to departmental grants, computing resources, and opportunities to collaborate on groundbreaking research projects.

Professional Growth: Support for attending top-tier AI/Data conferences, publishing research, and professional certifications.

Comprehensive Benefits: Full insurance coverage, academic leave, and retirement benefits in accordance with local labor laws.

Career Advancement: Clear progression paths to Associate Professor, Principal Investigator, or Program Director.

Data Science Lecturer, Assistant Professor of Data Science, AI/ML Instructor, Data Analytics Faculty, Data Science Program Lead.

Industry Keywords: Machine Learning, Deep Learning, Big Data, Data Engineering, Predictive Analytics, AI Ethics, Statistical Modeling, Data Visualization, Cloud Computing.

Attributes: Analytical, Innovative, Articulate, Collaborative, Technically-savvy, Research-driven.

Share

Comments