Business Development Associate (EdTech)
Practic-Alt is seeking a Business Development Associate to focus on institutional partnerships within the Indian education sector. The role involves managing relationships with schools and colleges, generating leads, and conducting presentations to key stakeholders. Candidates should have 1-2 years of experience in institutional sales and an existing network of educational contacts. This is a field-heavy role requiring strong communication and networking skills.
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Experience
1-2 years
Function
Business Development
Work mode
Onsite, India
Company
Tier 2
What you will work on
Practic-Alt is seeking a Business Development Associate to focus on institutional partnerships within the Indian education sector. The role involves managing relationships with schools and colleges, generating leads, and conducting presentations to key stakeholders. Candidates should have 1-2 years of experience in institutional sales and an existing network of educational contacts. This is a field-heavy role requiring strong communication and networking skills.
TAL's take
Standard business development role for an unfamiliar edtech firm with clear entry-level scope.
Well-defined responsibilities centered on institutional outreach and relationship management within the Indian education sector.
Must haves
- 1-2 years experience in EdTech or institutional sales
- Strong professional network within educational institutions
- Ability to pitch and present solutions clearly
- Confidence in institutional outreach and stakeholder interaction
- Excellent verbal and written communication skills
About the company
Unfamiliar company in the edtech space, default mid-tier assigned.
Posts mentioning Practic-Alt
Not practical knowledge
I cracked the interview based on my theoretical knowledge. The questions they asked and the scenarios they discussed, I was able to answer from a theoretical perspective. Now I have received the offer and will be joining soon. I’m just a bit concerned—will this backfire once I start working?
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Looking for practical advice only. No high level gyaan please. Just how to build right, get a team in place, mitigate risks and ship fast kind of advice
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.