In-Demand Data scientist Jobs in Parramatta Nsw

Machine Learning Engineer
Lucas G. Data Technologies —Sydney NSW
- $7,943.54 - $10,743.56 a month
- Full-time
- Azure
- Doctoral degree
- Computer Science
Quick Apply
22h
Marketing Analytics Lead
UpGuard —Sydney NSW
- $1,500 a year
- Full-time
- Remote
- Salesforce
- AI
- Leadership
- Annual leave
- Parental leave
AI Architect - Fabric
Intelligen Group —Sydney NSW
- $135,000 - $190,000 a year
- Full-time
- Hybrid work
- Azure
- AI
- Analytics
Quick Apply
AI Engineering Manager
MYOB —Sydney NSW
- Hybrid work
- Computer Science
- Master's degree
- AWS
- Parental leave
AI Engineering Manager
Axsys —Sydney NSW
- Computer Science
- Master's degree
- AWS
- Parental leave
Principal AI Engineer
MYOB —Sydney NSW
- Hybrid work
- Computer Science
- Master's degree
- AWS
- Parental leave
Senior AI Engineer
MYOB —Sydney NSW
- Hybrid work
- Computer Science
- Master's degree
- AWS
- Parental leave
Principal AI Engineer
Axsys —Sydney NSW
- Computer Science
- Master's degree
- AWS
- Parental leave
AI Solutions Architect
Baptistcare WA —Sydney NSW
- Full-time
- Hybrid work
- AWS
- No degree
- AI
3d
Chapter Area Lead (Executive Manager) - Home Buying AI
Commonwealth Bank —Sydney NSW
- Full-time
- Computer science
- Azure
- SQL
2d

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Machine Learning Engineer

Lucas G. Data Technologies
Sydney NSW

Quick Apply

Job Details

Permanent | Full-time
$7,943.54 - $10,743.56 a month
22 hours ago

Qualifications

CI/CD
Statistics
Data modelling
Azure
Doctoral degree
Computer Science
Engineering
Google Cloud Platform
Master's degree
SQL
AWS
Docker
Bachelor's degree
Deep learning
APIs
AI

Full Job Description

Machine Learning Engineer — Job Description

Position summary

Design, build, and productionize ML models and data pipelines to deliver scalable, reliable, and monitored machine‑learning features and products.

Key responsibilities

Model development: Research, prototype, and implement ML models (supervised, unsupervised, deep learning) tailored to product needs.
Data engineering: Ingest, clean, and feature‑engineer large datasets; design repeatable ETL/feature pipelines.
Productionization: Deploy models to production (model serving, APIs, batch jobs), optimize inference latency and throughput, and manage versioning.
Scalability &* reliability:* Design systems for horizontal scaling, fault tolerance, and cost efficiency (GPU/TPU usage, autoscaling).
Evaluation &* monitoring:* Define evaluation metrics, perform cross‑validation, A/B testing, and implement model monitoring (drift, performance, data quality).
MLOps &* CI/CD:* Build CI/CD pipelines for model training, testing, deployment, and rollback; automate retraining and deployment workflows.
Infrastructure &* tooling:* Work with cloud platforms and ML infrastructure (Kubernetes, Docker, MLflow, Sagemaker, Vertex AI) and optimize resource use.
Collaboration: Partner with data scientists, product managers, software engineers, and SRE to integrate models into products and services.
Interpretability &* fairness:* Implement model explainability, bias detection/mitigation, and privacy‑preserving techniques where applicable.
Research &* continuous learning:* Stay current with ML research and translate innovations into practical improvements.
Documentation &* governance:* Document model designs, assumptions, data lineage, and support compliance/audit requirements.

Required qualifications

Bachelor’s or Master’s degree in Computer Science, Statistics, Engineering, or related field (or equivalent experience).
2+ years experience deploying ML models to production (varies by seniority).
Strong programming skills in Python and experience with ML libraries (TensorFlow, PyTorch, scikit‑learn).
Experience with data processing tools (Pandas, Spark) and SQL.
Familiarity with model serving frameworks, containerization (Docker), and orchestration (Kubernetes).
Solid understanding of ML fundamentals (training, generalization, regularization) and evaluation metrics.

Preferred qualifications

Advanced degree (MS/PhD) in ML/AI, applied statistics, or related field.
Experience with cloud ML platforms (AWS SageMaker, GCP Vertex AI, Azure ML) and MLOps tools (MLflow, Kubeflow).
Experience with deep learning at scale, transformers, or specialized domains (NLP, CV, recommender systems).
Background in software engineering best practices, distributed systems, and performance optimization.
Familiarity with privacy techniques (differential privacy, federated learning) and regulatory considerations.

Pay: $7,943.54 – $10,743.56 per month

Work Location: In person

Quick Apply

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data scientist jobs in parramatta nsw

Machine Learning Engineer

Marketing Analytics Lead

AI Architect - Fabric

AI Engineering Manager

AI Engineering Manager

Principal AI Engineer

Senior AI Engineer

Principal AI Engineer

AI Solutions Architect

Chapter Area Lead (Executive Manager) - Home Buying AI

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