Data Science Trainer

Job Overview:

We are seeking an experienced and passionate Data Science Trainer to join our team and deliver comprehensive training programs on Data Science, Machine Learning, Deep Learning, and related technologies. The ideal candidate will have a strong background in data science concepts, practical industry experience, and a proven ability to teach complex topics effectively. This role involves designing and conducting interactive sessions based on our structured syllabus, guiding learners through hands-on projects, and fostering a deep understanding of data-driven problem-solving. 

Key Responsibilities:

Deliver Engaging Training Sessions:

Hands-On Demonstrations and Projects:

Curriculum Alignment and Customization:

Mentor and Assess Learners:

Incorporate Best Practices:

Stay Updated and Innovate:

Administrative Duties:

Required Qualifications:

Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, or a related field. A PhD is a plus.

Certification in Data Science, Machine Learning, or related areas (e.g., from Coursera, edX, or Google) is desirable.

Essential Skills and Experience:

Technical Expertise:

Proficiency in Python programming, including data types, control structures, loops, functions, NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, TensorFlow, Keras, and libraries for NLP (e.g., NLTK, spaCy) and generative AI (e.g., Hugging Face Transformers).

Strong knowledge of statistics:

Expertise in Machine Learning:

Deep Learning proficiency:

NLP skills:

Familiarity with data handling:

Teaching and Communication Skills:

3+ years of experience as a trainer, instructor, or mentor in Data Science/ML courses, preferably in academic or corporate settings.

Ability to explain complex concepts simply, using real-world use cases (e.g., business problems in mobile/banking).

Experience with interactive tools like Google Colab for in-class exercises and AI-assisted learning.

Professional Experience:

5+ years in Data Science or related roles, with hands-on project experience in ML model development, deployment, and optimization.

Proven track record of working on diverse datasets and case studies, including overfitting mitigation, feature engineering, and hyperparameter tuning (e.g., Grid Search CV).

Soft Skills:

Excellent presentation and interpersonal skills to engage diverse audiences.

Problem-solving mindset with a focus on practical, industry-relevant training.

Ability to adapt to learner needs, handle Q&A effectively, and promote collaborative learning.

Preferred Qualifications

Industry experience in deploying ML models or working with big data tools.

Publications, contributions to open-source projects, or speaking engagements in Data Science conferences.

Familiarity with ethical AI practices and bias mitigation in models.