Business Development Executive

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.

Referral for DevOps Engineer | Cloud Operations Engineer | Cloud Developer | Cloud Engineer with 2.5 years of experience at Cognizant

After 2.5 years of working in Cognizant, building cloud-native solutions across AWS, Azure and ,GCP, I’m now looking for my next challenge in Multi-Cloud DevOps, Automation, and Infrastructure as Code (IaC). Throughout my journey, I’ve had the opportunity to: ✅ Automate cloud infrastructure using Terraform, Pulumi, and GitOps, ensuring efficiency and consistency. ✅ Optimize multi-cloud environments, achieving cost savings of 45% and improving resource management. ✅ Build CI/CD pipelines with Jenkins, GitHub Actions, and SonarQube to streamline software delivery. ✅ Deploy containerized applications with Docker, Kubernetes, and Helm, reducing deployment time by 70%. ✅ Enhance cloud security & governance, implementing best practices in compliance and drift detection. I’m now actively looking for DevOps roles where I can contribute my expertise in AWS, Kubernetes, Terraform, and automation to build scalable and high-performing platforms. If you’re hiring or know of an opportunity that aligns with my skill set, I’d love to connect! Please feel free to reach out or tag relevant connections in the comments. Let’s build something great together!

Why is it easier for people to keep options open, or quietly drift away but so difficult to have one honest conversation?

To just say that I’m under pressure from my parents, I don’t think we align on a few things, I’m not ready for this. Yes, it’s uncomfortable or might lead to arguments. But isn’t that still better than creating confusion for someone who’s genuinely invested? If a decision is being influenced heavily by family or pressure, shouldn’t that be openly acknowledged instead of being masked with other reasons? Because at the end of the day, it’s not the parents or society or ex living that relationship. It’s two people. So is avoiding honesty just easier in the short term? or have we started normalizing it?