Social Media Intern
Meragi is a leading wedding planning startup in India seeking a creative Social Media Intern to join their team in Bengaluru. The role involves managing content across Instagram and YouTube, including conceptualizing reels and coordinating with creative teams. The candidate will engage with the brand's online community and monitor social media trends. This is a 3-6 month internship focused on digital storytelling and audience growth.
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Experience
0-0 years
Function
Marketing
Work mode
Onsite, India
Company
Tier 2
What you will work on
Meragi is a leading wedding planning startup in India seeking a creative Social Media Intern to join their team in Bengaluru. The role involves managing content across Instagram and YouTube, including conceptualizing reels and coordinating with creative teams. The candidate will engage with the brand's online community and monitor social media trends. This is a 3-6 month internship focused on digital storytelling and audience growth.
TAL's take
Solid consumer startup with clear role definition for an intern looking to gain experience in creative marketing.
The JD provides a very clear scope of responsibilities, specific platforms, and creative expectations for the intern role.
Salaries at Meragi
17.1 LPA average
Based on 4 Grapevine salary entries for Meragi.
Operations
2 - 4 years
6 LPA average
Range: 6 - 6 LPA
Engineering
2 - 4 years
22 LPA average
Range: 22 - 22 LPA
Other roles
4 - 6 years
23 LPA average
Range: 23 - 23 LPA
Other roles
8 - 10 years
17 LPA average
Range: 17 - 17 LPA
Must haves
- Basic understanding of social media platforms and content trends
- Strong communication and creative thinking skills
- Ability to work in a fast-paced and creative environment
Tools and skills
Nice to have: video editing apps.
About the company
Established wedding planning startup with VC backing and a strong consumer presence in India.
Posts mentioning Meragi
Title: Data Scientist (~2 YOE) – Built planning tools, pipelines, and AI system. Need honest feedback on profile.
Hi all, Looking for practical feedback on my profile before I start applying. I’ll keep this structured so it’s easier to evaluate. --- 1) Planning Tools / Web Applications Problem: Forecasting workflows were fragmented and heavily Excel-driven: - Multiple data sources (orders, shipments, different forecast versions) - Manual merging, lookups, and adjustments - No way to simulate scenarios or compare forecasts cleanly - Different planners using different methods → inconsistency What I built: - Two internal applications for planning workflows: - A planning tool integrating 8+ data sources - A forecasting simulator supporting multi-level editing (high → granular) Key capabilities: - Real-time scenario simulation - Side-by-side comparison of multiple forecast types - Hierarchical adjustments across levels - SQL write-back for persistence Scale: - Processes ~150K+ records per cycle - Used in monthly planning cycles by multiple teams Impact: - Removed fragmented Excel workflows - Enabled consistent decision-making across users - Reduced manual effort and improved visibility into forecast behavior --- 2) Automation & Data Pipelines Problem: Core workflows were manual and repetitive: - Multi-file Excel processing - Data cleaning + merging across systems - Version tracking errors - High effort per cycle (1–4 hours depending on workflow) What I built: - Multiple pipelines automating end-to-end workflows Examples: - Large-scale consolidation pipeline: - Input: ~1M+ rows across 20+ files - Output: clean, unified dataset (~75% reduction) - 2nd pipeline: - Replaced a 23-step manual process - Standardized inconsistent formats across datasets - 3rd automation processing: - Automated unpivoting, enrichment, and version tracking Impact: - Reduced processing time from hours → minutes per cycle - Eliminated manual errors (copy-paste, lookup mistakes) - Standardized workflows across users --- 3) Power BI / Monitoring Problem: Recent data (orders/shipments) showed inconsistencies, but: - No visibility into changes over time - Hard to identify where data drift was happening What I built: - Power BI dashboards with: - Hierarchical filters - Drill-down views - Month-over-month comparison Scale: - ~30K+ records analyzed Impact: - Enabled early detection of data inconsistencies - Helped planners validate inputs before forecasting - Improved trust in upstream data --- 4) Side Project (AI System) What I built: - AI-powered job assistant system Features: - Scrapes job postings - Scores relevance using LLMs - Generates tailored resume points and outreach messages - Tracks applications Tech: - FastAPI backend - LLM routing (cloud + local fallback) - SQLite storage Goal: - Build a system-driven workflow (not just model usage) --- My concern Most of my work sits at the intersection of: - forecasting - data systems - workflow automation I’m trying to move into: 👉 Applied Data Scientist / Product-oriented roles --- Questions 1. Does this profile look too niche (forecasting-heavy)? 2. Does “building systems around data” help or hurt for DS roles? 3. What’s the biggest gap you see (if any)? Would really appreciate honest feedback. Thanks.
6 + years experience as a Networking engineer (Managing Network devices, Routing, Switching, Wireless, Access point, WLC, Ip management, DNS, DHCP, Meraki & Fortinet Firewalls
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