Data Science Trainer

Position Summary:

We are seeking a highly skilled and passionate Data Science Trainer to join our instructional team. In this role, you will be responsible for delivering comprehensive, hands-on data science training to a diverse audience ranging from beginners to advanced learners. You will empower students with practical skills in statistics, data analysis, machine learning, Python programming, and data visualization to help them succeed in academic, professional, or industry-aligned data roles.

This role is ideal for someone who loves teaching, mentoring, and staying current with data science tools and trends. Whether you’re training students in-person, online, or through blended learning formats, your mission will be to translate complex data concepts into real-world applications.

Key Responsibilities:

1. Training & Instruction

Deliver engaging and interactive lessons, workshops, and bootcamps on core data science topics including:
- Python programming for data analysis
- Data wrangling and cleaning
- Statistics and probability
- Machine learning algorithms and model evaluation
- Data visualization using libraries like Matplotlib, Seaborn, Plotly
- Working with databases, SQL, and cloud platforms
- Tools like Pandas, NumPy, Scikit-learn, TensorFlow, Keras, Jupyter Notebooks
Adapt teaching methods to fit various formats (in-person, live online, self-paced content).
Guide learners through hands-on projects and capstone assignments.

2. Curriculum Development & Content Creation

Design and update curriculum materials, lesson plans, coding exercises, assessments, and projects.
Ensure the curriculum aligns with current industry standards and reflects modern tools, techniques, and best practices.
Contribute to the development of online content, recorded lectures, quizzes, and student guides.

3. Student Engagement & Mentorship

Provide one-on-one support, code reviews, and mentorship to help students grasp difficult concepts.
Give constructive feedback on assignments, projects, and assessments.
Foster an inclusive, encouraging, and collaborative learning environment.
Monitor student progress and provide reports or recommendations as needed.

4. Industry Alignment & Innovation

Stay up to date with industry trends, new tools, and emerging technologies in the field of data science.
Identify areas of improvement and continuously enhance training delivery.
Collaborate with program managers, fellow trainers, and industry partners to ensure relevance and impact of learning outcomes.

Minimum Qualifications:

Bachelor’s degree in Computer Science, Data Science, Mathematics, Statistics, Engineering, or a related field.
2–5+ years of experience in data science, analytics, machine learning, or a related technical field.
Proficient in Python and common data science libraries (NumPy, Pandas, Scikit-learn, etc.).
Strong understanding of core machine learning concepts and algorithms.
Experience working with real-world datasets and data science projects.

Preferred Qualifications:

Master’s degree or PhD in a quantitative discipline.
Prior teaching, mentoring, or technical training experience.
Familiarity with cloud platforms (AWS, Azure, GCP), Git/GitHub, and APIs.
Experience with deep learning frameworks (TensorFlow, PyTorch).
Knowledge of Power BI, Tableau, or other data visualization tools.
Certification in data science or instructional design is a plus (e.g., Google Data Analytics, Microsoft Data Science, Coursera specializations).

Essential Skills:

Excellent verbal and written communication skills.
Strong presentation, public speaking, and facilitation abilities.
Ability to explain complex technical topics in an accessible and engaging way.
Passion for education, innovation, and helping others succeed.
Organized, self-motivated, and adaptable in a fast-paced environment.

Work Environment & Conditions:

May involve in-person instruction, live remote sessions, or asynchronous content development.
Flexible hours, including evenings or weekends, depending on student or client schedules.
Collaboration with cross-functional teams including instructional designers, data scientists, and developers.
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