Available for new opportunities

Rusan
Vaidya

Data Scientist & Engineer who turns
messy data into clear decisions.
Based in Sydney · ML · Cloud · Reliability Engineering.

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Rusan Vaidya
Current Role
Data Analyst @ IAEngg
Specialisation
ML + Cloud
CV · Reliability · AWS
Location
🇦🇺 Sydney, AU
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01 — About

The person
behind the pipeline

I'm a data scientist who grew up in Nepal and made my way to Sydney building systems that make asset infrastructure smarter. I genuinely enjoy the full journey — from raw ingestion to the moment a model catches a defect before an engineer even sets foot on site.


Right now I'm building AI computer vision models for asset defect detection and Weibull reliability analysis systems at IAEngg. Before that, I built cloud data platforms at Transformd, and got my hands dirty with Snowflake and Tableau at Yomari in Kathmandu.


I care as much about the architecture as the insight — clean pipelines, honest metrics, and models that actually ship into production.

🏔️

Kathmandu → Sydney

Brought the Himalayan patience to Australian infrastructure problems

🔍

Computer Vision for the physical world

Object detection models catching real asset defects from inspection images

📉

Reliability engineering meets ML

Weibull analysis + Health Index models for lifecycle & replacement planning

☁️

Cloud data platform builder

AWS S3 · Glue · Athena · Redshift · QuickSight — end to end

🗃️

Data quality obsessive

SCD methods, schema design, deduplication — the unsexy stuff done right

0
Years Experience
0
Companies
0
Core Technologies
0
% Remote Ready
02 — Stack

Technologies I
actually use

🐍
Python
☁️
AWS
🔍
Computer Vision
🔥
PyTorch
🧊
TensorFlow
🗄️
SQL / Redshift
❄️
Snowflake
⚡
PySpark
📊
Tableau / QuickSight
🍃
MongoDB
🐳
Docker / CI/CD
📈
Reliability Analysis
03 — Experience

Where I've built
things that matter

Sep 2025 — Present
Data Analyst
Innovative Assets Engineering (IAEngg) · Sydney, AU
  • Performed Weibull reliability analysis on historical failure data to estimate probability of failure and improve lifecycle planning decisions
  • Developed Weibull-based decay models enabling risk-based maintenance prioritisation and capital replacement forecasting
  • Built AI computer vision models to detect asset defects from inspection images, improving consistency and accelerating condition assessment workflows
  • Designed and trained object detection models for component and defect identification across large-scale inspection image datasets
  • Developed a Health Index model integrating AI predictions with reliability metrics to support data-driven maintenance and replacement decisions
Jul 2024 — Aug 2025
Data Engineer Analyst
Transformd · Sydney, AU · Hybrid
  • Built a centralised data foundation consolidating raw data from multiple financial institutions into a unified platform, enhancing accessibility and reporting efficiency
  • Established a curated data layer where raw data was cleaned, organised, and made analytics-ready to support internal discovery initiatives
  • Built a centralised data warehouse supporting both internal teams and financial clients, handling growing volumes of complex datasets reliably
  • Enhanced dashboarding experience delivering faster, more interactive, client-aligned insights compared to the legacy analytics platform
  • Collaborated with product and analytics teams to align reporting with business and client needs, improving dashboard relevance and usability
May 2022 — Aug 2022
Data Software Engineer
LIS Nepal Pvt Ltd (Yomari Company) · Kathmandu, NP · Internship
  • Launched the organisation's first data-driven initiative with a unified Snowflake-based platform and automated ETL pipelines from CSV and Excel sources
  • Set up core data quality frameworks and performance optimisations in Snowflake for reliable, cost-effective reporting
  • Designed interactive Tableau dashboards tracking sales pipeline dynamics, product feature adoption, and customer retention
  • Conducted customer segmentation and churn analyses on behavioural, demographic, and product usage data to inform product strategy
  • Managed slowly changing dimensions (SCD) in customer and product datasets to accurately track historical changes without data loss
Mar 2020 — May 2022
Software Analyst
Healing R. Nepal · Lalitpur, NP · Hybrid
  • Led stakeholder collaboration to deliver iterative backend solutions automating manual workflows and improving operational efficiency
  • Designed and developed scalable backend APIs and automated notification systems using FastAPI and Django
  • Implemented database management and optimisation strategies for PostgreSQL and MongoDB, ensuring data integrity and query performance
  • Established structured CI/CD pipelines with Docker and GitHub Actions, enabling reliable and consistent delivery of data services
04 — Projects

Things I've built
and broken

↗
01
Asset Defect Detection CV Model
Computer vision pipeline for detecting infrastructure defects from inspection images. Object detection models trained on large-scale image datasets, replacing manual inspection workflows with consistent, scalable AI assessment.
PyTorchObject DetectionDeep LearningPython
↗
02
Health Index & Reliability System
Weibull reliability analysis combined with AI predictions into a unified Health Index model. Enables risk-based maintenance prioritisation and data-driven capital replacement forecasting for physical assets.
Weibull AnalysisReliability Eng.MLPython
↗
03
Financial Data Platform (Transformd)
End-to-end cloud data platform consolidating data from multiple financial institutions. Built on AWS S3, Glue, Athena, and Redshift — with a curated analytics layer powering faster, more interactive client dashboards.
AWS S3GlueAthenaRedshiftQuickSight
↗
04
Snowflake DWH + ETL (Yomari)
Launched the organisation's first data-driven initiative — a unified Snowflake warehouse with automated ETL from raw files, SCD dimension management, Tableau dashboards, and customer segmentation analysis.
SnowflakePySparkTableauSCD
↗
05
FastAPI Backend + CI/CD Platform
Scalable backend APIs and automated notification systems built with FastAPI and Django. Established Docker-based CI/CD pipelines via GitHub Actions, with PostgreSQL and MongoDB powering data integrity at scale.
FastAPIDjangoDockerPostgreSQLMongoDB
↗
06
Customer Churn & Segmentation Analysis
Comprehensive churn modelling on behavioural, demographic, and product usage data for a luxury fashion brand. Insights directly informed feature offerings and customer engagement strategy.
PythonSegmentationChurn AnalysisTableau
05 — Playground

Ask me anything
about data

An interactive terminal. Ask anything about my experience, skills, or approach.

rusan@portfolio ~ $
rusan@portfolio ~ $ whoami
Data Scientist & Engineer. AWS + ML + Python. Based in Sydney.
rusan@portfolio ~ $ cat skills.txt
Python | AWS (S3/Athena/Redshift/Glue/QuickSight) | PyTorch | TensorFlow
SQL | MongoDB | Snowflake | PySpark | Streamlit
rusan@portfolio ~ $ _
06 — Contact

Let's build something
worth building

Whether it's a data problem, an engineering challenge, or just a conversation about
why your pipeline keeps failing at 3am — I'm around.

✉ [email protected] in LinkedIn ⌥ GitHub