Facilitator Data Analytics & Data Science

Position Overview

The Facilitator Data Analytics & Data Science plays a critical role in delivering highquality academic support and learning experiences to students enrolled in the Data Analytics program. The Facilitator is responsible for conducting live instructional sessions, supporting students through on-demand tutoring and video explanations, performing quality assurance reviews, and contributing to learner engagement initiatives.

This role requires strong analytical expertise, structured problem-solving ability, and the capability to explain complex data concepts clearly to beginner and intermediate learners. The Facilitator will collaborate closely with mentors and academic teams to improve instructional delivery quality, support newly onboarded experts (MNEs), and contribute to curriculum enhancement aligned with evolving industry expectations.

Candidates with strong academic backgrounds and research orientation particularly PhD holders or candidates currently pursuing a PhD in Data Science, Statistics, Mathematics, Computer Science, or related domains will be given preference

Eligibility Criteria

• Minimum 3+ years of relevant industry and/or teaching experience in Data Analytics or related domains
• Strong hands-on experience with modern analytics workflows and business data interpretation
• Preference will be given to candidates holding a PhD or pursuing a PhD in Data Science, Statistics, Mathematics, Computer Science, or related disciplines
• Bachelors or Masters degree in Data Analytics, Statistics, Mathematics, Computer Science, Engineering, or related domains preferred
• Prior experience in EdTech or online instruction is an added advantage • Strong communication skills with the ability to teach US-based learners
• Experience conducting sessions using Zoom or similar virtual learning platforms
• Strong organizational and time-management skills
• Ability to explain complex analytical topics clearly without unnecessary jargon
• Strong written and verbal English proficiency
• Willingness to work rotational shifts and week-offs based on business requirements
• Ability to mentor junior experts and contribute to academic quality improvement initiatives

Primary Responsibilities

• Perform all core academic support activities including: Live Lessons, Weekly Video Tutoring, On-Demand Tutoring Support and QA Review
• Maintain a minimum QA score of 80% or above across all assigned activities
• Deliver high-quality responses aligned with academic standards during live support interactions
• Review mentor session content based on updated internal guidelines • Provide structured feedback on externally sourced academic content across all difficulty levels
• Support students through on-demand video explanations
• Conduct interactive Q&A sessions to enhance conceptual clarity
• Participate in community engagement initiatives to support learner success
• Identify learner knowledge gaps and escalate insights to Leads
• Train and mentor MNEs during their first 90 days of onboarding
• Provide continuous process support to MNEs in their day-to-day academic activities
• Collaborate with mentors and academic teams to strengthen delivery quality
• Suggest improvements to: o activity guidelines o internal processes o learning workflows
• Contribute recommendations for curriculum updates and new course additions aligned with market demand
• Monitor incoming academic workload and recommend shift redistribution when required
• Prepare contribution reports on: o SME academic effectiveness o live support engagement quality
• Curate emerging analytics tools, datasets, and workflows relevant to Data Analytics programs

Secondary Responsibilities

• Evaluate video support interactions, live lesson delivery quality, and on-demand tutoring responses as per defined QA standards
• Present structured insights and recommendations supporting process improvement initiatives and academic delivery effectiveness
• Assist in strengthening instructional delivery frameworks, learner engagement strategies, and overall academic support quality
• Support curriculum alignment with evolving data analytics industry expectations and hiring trends
• Assist in identifying opportunities to improve learner experience across analytics learning pathways

Subject Expertise Requirements Data Analytics & Data Science Candidates should demonstrate strong working knowledge across the following analytics domains:

Data Analysis Foundations

• Data retrieval and querying techniques
• Data cleaning and preprocessing workflows
• Exploratory data analysis (EDA)
• Statistical reasoning and interpretation
• Feature Engineering

Core Tools & Technologies

Industry-level proficiency in:

• SQL • Advanced Excel
• Tableau
• Python
• PostgreSQL
• PowerPoint
• IDEs such as Google Colaboratory and pgAdmin
• ChatGPT, Gemini, Claude, Google Assistant, etc.

Statistical & Experimental Methods

Working knowledge of:

• Statistical analysis fundamentals
• Experimental design
• Hypothesis testing
• A/B testing methodologies
• Data interpretation for business decision-making

Data Visualization & Communication

Strong ability to:

• Communicate business insights from datasets
• Create structured dashboards and reports
• Build effective visual storytelling using analytics tools
• Translate analytical outputs into actionable recommendations

Advanced Technologies

Strong knowledge of:

• Machine Learning
• Deep Learning
• Natural Language Processing
• Large Language Models
• Prompt Engineering
• AI Workflow and Ethics
• PySpark
• ETL Processes
• API Requests using Python
• Big Data (Hadoop and Spark)
• Time Series Analysis

Data Handling & Workflow Skills

Hands-on experience with:

• Data extraction workflows
• Data cleaning pipelines
• Dataset transformation techniques

Education Requirements

• B.Tech / BE / BSc / MSc / M.Tech in Data Analytics, Data Science Statistics, Mathematics, Computer Science, Engineering, or related domains
• Certifications in Data Analytics / Data Science / Business Analytics preferred
• Minimum 15 years of full-time education

Key Competencies

Ideal candidates should demonstrate:

• Strong learner empathy

• Structured analytical explanation ability

• Clear business insight communication skills

• Dashboard review and feedback capability

• Communication clarity for live instruction

• Collaboration with mentors and SMEs

• Continuous learning mindset aligned with evolving analytics tools and practices

• Strong student advocacy and coaching orientation in fast-paced learning environments