Business Development Executive

Top 3 questions to ask before choosing your new role.

Ofcourse Work, Pay, Location are basics however lately i have started asking: 1. How much Runway you have and what is the key reason you are not profitable (in case they are not) 2. Is this a newly opened position, if not, why did the last person left? 3. What’s your attrition rate, and how open are you in terms to taking feedback? I know there are ways to just pass these questions however it gives you a better picture about the org, what are you some questions that one should start asking? Let’s make this a good playbook for all the folks :)

Cautious Start for Indian Markets Amid Global Weakness

- **Sensex and Nifty 50** expected to open cautiously on Thursday due to weak global cues. - **Gift Nifty** indicates a gap-up start, trading around 24,540, a 60-point premium. - **Wednesday's session** ended with marginal losses; Sensex fell 0.17%, Nifty 50 down 0.15%. - **Technical analysis** suggests potential short-term upside bounce but overall weak trend. - **Global markets**: Asian markets down, US stocks fell with Dow Jones dropping 0.96%. Source: [Mint](https://www.livemint.com/market/stock-market-news/nifty-50-sensex-today-what-to-expect-from-indian-stock-market-in-trade-on-october-24-11729736723351.html), [Mint](https://www.livemint.com/market/stock-market-news/indian-stock-market-8-key-things-that-changed-for-market-overnight-gift-nifty-strong-us-dollar-to-treasury-yields-11729735157575.html)

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.