Nitish C

Assisting in data cleaning, transformation, and KPI reporting for multiple business datasets.
Building Power BI/Tableau dashboards to improve visibility into operational metrics for internal teams.
Supporting reporting automation using SQL, Excel, and Python to reduce manual effort and improve data accuracy.
Collaborating with senior analysts to generate insights and prepare documentation for business stakeholders.

• Generated 25+ weekly reports and 5 executive dashboards using Python and SQL, supporting operational decisions for 200+migrant workers.• Managed datasets of 10,000+ records via Google Sheets and Freshsales CRM, maintaining 99.5% data accuracy across 15+ datafields.• Implemented automation solutions for migrant job allocation, reducing processing time by 75% and operational overhead by$15K annually.

Generated 25+ weekly reports and 5 executive dashboards using Python and SQL, supporting operational decisions for 200+ migrant workers.
Managed datasets of 10,000+ records via Google Sheets and Freshsales CRM, maintaining 99.5% data accuracy across 15+ data fields.
Implemented automation solutions for migrant job allocation, reducing processing time by 75% and operational overhead by $15K annually.

Designed a fully interactive Power BI dashboard for Toyota to monitor project timelines, manpower utilization, activity progress, and PIC workload.
Built 15+ visual components, including Gantt-style progress charts, completion trend lines, doughnut status summaries, and KPI cards.
Integrated monthly man-hours, manpower, and activity-level data to provide real-time visibility into project execution across multiple teams.
Improved management decision-making by enabling variance tracking, activity-level bottleneck identification, and PIC-level workload balancing.
Delivered a professional-grade dashboard UI with filters, drilldowns, and branded themes customized for Toyota.

Designed a Google Sheets–based system with 12 Apps Script functions; processed 500+ daily job allocations with a 95% automation rate.
Reduced manual errors by 80% and decreased processing time from 4 hours to 45 minutes through automated workflows and validation.
Developed a centralized dashboard used by 8 team leads to monitor 1,200+ tasks monthly across 3 geographic regions.

Annotated 2,000+ wagon images using Supervisely/Label Studio to build a high-quality segmentation dataset.
Trained YOLOv8-seg and RT-DETR models for wagon damage detection, achieving up to 60% accuracy (mAP50, mAP50-95).
Performed dataset preprocessing, label validation, class balancing, and augmentations to improve model stability.
Conducted training experiments (20–120 epochs) with hyperparameter tuning for learning rate, batch size, and augmentations.
Analyzed validation curves, confusion matrices, and error cases to refine model performance and reduce misclassification.
Documented the end-to-end ML workflow, including dataset preparation, training logs, tuning strategy, and model comparisons.

Analyzed coal price data from 7 global markets over a 24-month period using Python/SQL; processed 50,000+ price points and identified 12 key risk factors.
Built 15+ interactive Power BI/Tableau visualizations tracking monthly procurement spend with 85% forecast accuracy.
Delivered procurement strategy recommendations resulting in a 10% profit increase and 20% coal sales revenue growth within 6 months.

Analyzed 2,600+ steel heats across 8 production lines using Python/Excel; automated quality control for 15 chemical elements with 98% accuracy.
Designed a 4-platform dashboard ecosystem serving 25+ metallurgists with real-time monitoring of 150+ daily production batches.
Established a predictive quality system reducing manual inspection by 60% (saving 120 hours/month) and improving compliance from 85% to 96% across 5 steel grades.

Engineered an 8-page Power BI dashboard tracking 12 KPIs across 3 production shifts for 50+ operators processing 800+ units daily.
Implemented a Python preprocessing pipeline handling 25,000+ records monthly; improved data quality from 72% to 94% through statistical methods.
Identified quality bottlenecks affecting 15% of production: observed 35% higher rejection with 3 specific ink types and achieved significant annual cost reduction through targeted fixes.

Built a Power BI dashboard to analyze material movement and track procured, consumed, and unused quantities.
Cleaned and standardized inventory data by mapping movement types and creating DAX-based utilization metrics.
Identified excess and underutilized materials, providing recommendations that reduced inventory and operational costs.
Delivered insights that enhanced procurement planning and improved stock utilization efficiency.