Data Science Intern

Bright Network Consulting; Australia; Remote; AUD 90,000 - 150,000; Full time

Job summary

Bright Network Consulting seeks a Data Scientist for a fully remote role based in Australia. You will transform large datasets into actionable insights, build ML models, and deliver business-ready analytics. You will work with Python, SQL, and visualization tools (Power BI/Tableau) to drive data-driven decisions across teams.

A strong foundation in statistics and problem-solving is essential for success. Collaborate with data engineers, analysts and developers; monitor model performance and

Qualifications

• A degree in Data Science, CS, Statistics or Mathematics.
• Strong programming and algorithm knowledge across data pipelines.
• Experience with data cleaning, EDA and modelling on real datasets.

Responsibilities

• Collect, clean, preprocess, and analyse large datasets from multiple sources.
• Develop, train, test, and optimise ML models for predictive analytics.
• Create dashboards and visualisations using Power BI, Tableau, Matplotlib, or Seaborn.
• Write and optimise SQL queries to extract and manage data.
• Translate business requirements into analytical solutions and communicate findings.

Skills

Python

SQL

Statistics

Pandas

NumPy

Scikit-learn

Data Visualization

Education

Bachelor's in Data Science

Computer Science / Mathematics

Tools

Power BI

Tableau

Matplotlib

Seaborn

SQL queries (optimisation)

Job description

We are hiring a skilled and motivated Data Scientist on behalf of one of our clients for a fully remote opportunity.

The ideal candidate will have strong analytical and problem-solving skills, hands-on experience with data science tools, and the ability to transform complex datasets into actionable business insights.

Job Title: Data Scientist

Location: Australia

Work Mode: 100% Remote

Key Responsibilities

• Collect, clean, preprocess, and analyse large datasets from multiple sources.
• Perform exploratory data analysis (EDA) to identify trends, patterns, and business opportunities.
• Develop, train, test, and optimise machine learning models for predictive analytics and forecasting.
• Apply statistical techniques and hypothesis testing to support data-driven decision-making.
• Build predictive models and implement algorithms to solve business problems.
• Create dashboards, reports, and visualisations using Power BI, Tableau, Matplotlib, or Seaborn.
• Write and optimise SQL queries to extract and manage data from databases.
• Use Python libraries such as Pandas, NumPy, Scikit-learn, and SciPy for data analysis and modelling.
• Identify and resolve data quality issues to ensure accuracy and reliability.
• Collaborate with data engineers, business analysts, developers, and other stakeholders.
• Translate business requirements into analytical solutions and communicate findings effectively.
• Monitor model performance and continuously improve machine learning solutions.
• Automate data processing and reporting workflows to improve efficiency.
• Perform feature engineering and feature selection to enhance model performance.
• Document analytical processes, methodologies, and project outcomes.
• Ensure data privacy, security, and compliance with applicable data protection requirements.

Required Skills & Qualifications

• Strong programming skills in Python and SQL.
• Good understanding of statistics, probability, and machine learning algorithms.
• Hands-on experience with Pandas, NumPy, Scikit-learn, and data visualisation libraries.
• Familiarity with Power BI or Tableau.
• Excellent analytical, problem-solving, and communication skills.
• A degree in Data Science, Computer Science, Statistics, Mathematics, or a related field is preferred.

Preferred Qualifications

• Experience working with real-world datasets, predictive modelling, and advanced statistical analysis.
• Familiarity with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform (GCP).
• Knowledge of deep learning frameworks such as TensorFlow, PyTorch, or Keras.
• Experience with natural language processing (NLP), computer vision, or generative AI applications.
• Understanding of big data technologies such as Apache Spark, Hadoop, or Databricks.
• Experience with ETL pipelines, data integration, and data preprocessing workflows.
• Familiarity with database systems such as PostgreSQL, MySQL, MongoDB, or Snowflake.
• Knowledge of model deployment using Flask, FastAPI, Docker, or cloud-based services.
• Understanding of MLOps practices, including model versioning, monitoring, and lifecycle management.
• Experience using Git or other version control systems for collaborative development.
• Familiarity with A/B testing, experimental design, and business performance metrics.
• Ability to work with time-series data, forecasting techniques, and anomaly detection.
• Experience developing scalable and production-ready machine learning solutions.
• Understanding of responsible AI practices, model explainability, and bias detection.
• Experience working in Agile environments and collaborating with cross-functional teams.
• A portfolio of data science projects, research work, or contributions to open-source projects is an advantage.