Data Scientist

MS Data Sci student with 6mon+ internship exp. Skilled in Python & SQL. Eager to solve real-world problems with data.
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Work expectations

Status
Actively looking
Not looking
Not looking
Open to new opportunities
Location
Melbourne
-
Open to relocating
Commitment
Casual / Contract / Part-time, Internship
Availability from time of offer
In 1 month or less
Years of experience
Less than one year
Top 3 languages or frameworks
Python, SQL, AWS
Also knows
Tableau
Preferred work environment
Hybrid, Fully remote, In the office
Strengths
Interested in working involving

Company that works specifically with data

Not interested in work involving

Other information
Able to work in Australia without visa sponsorship
Requires visa sponsorship to work in Australia

Pay expectations

Full-time
$
60000
per year
Casual / Contract
$
30
per hour
Pay is negotiable in exchange for equity
Pay is negotiable in exchange for equity
Pay is not negotiable in exchange for equity
Open to volunteering or working for equity only
Not interested in volunteering or working for equity only if joining as a co-founder

Referred by

  •  

Work experience

Proudest professional achievement

Data Engineer / ML Engineer

Energy market analytics company

  • Built Python and SQL analytics tools to extract and transform large-scale energy market data from AEMO and NEMED, enabling business analysis across cross-functional stakeholders
  • Architected a serverless ETL pipeline (Azure Functions + Cosmos DB) to automate market and weather data ingestion every 30 minutes, saving ~20 hours per week and reducing infrastructure costs
  • Engineered 25+ predictive features using scikit-learn and pandas — including cyclic encodings, rolling volatility metrics, and price-spike indicators — to capture non-linear market regimes for short-term price forecasting
  • Tuned CatBoost models via cross-validation and unseen test data, reducing forecasting error to ≈$3/MWh RMSE and improving model reliability for business decision-making
  • Presented modelling trade-offs and deployment risks to non-technical stakeholders, influencing the decision to delay production release
  • Deployed model to a live Azure ML REST endpoint; predictions auto-written to Cosmos DB and benchmarked against actuals every 30 minutes, completing a full end-to-end ML lifecycle from ingestion to production monitoring

Education

Master of Data Science | Monash University exp(Nov 2026)
Bachelor of Biomedical Science | Monash University

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