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.

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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

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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

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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

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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)

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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

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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.