The Role

We are looking for a curious and technically capable Data Scientist to join a growing consulting team at a Data and Analytics consultancy. the role can be based in Sydney or Melbourne

As a Data Scientist, you will work across a range of client engagements, developing quantitative models and applying them to real business decisions. You will collaborate with consultants, data engineers, architects, analysts and client stakeholders.

You will be responsible for detailed analysis, model development, testing and documentation.

Skills & Experience

Essential:

- Strong Python development skills.
- Strong foundations in applied statistics, probability and mathematical modelling.
- Experience with numerical and data analysis libraries such as NumPy, pandas, Polars,

SciPy or statsmodels.

- Experience with Monte Carlo simulation, resampling or scenario generation.
- Understanding of time series analysis and forecasting.
- Experience calibrating and validating quantitative models.
- Ability to perform sensitivity analysis, stress testing and uncertainty quantification.
- Strong SQL and experience working with large or detailed datasets.
- Familiarity with Git, modular code, automated testing and CI/CD practices.
- Strong analytical and problem solving skills.
- Ability to communicate technical concepts to both technical and nontechnical stakeholders.
- Ability to learn new business domains and work effectively with subject matter experts.
- Ability to manage priorities within a consulting or project delivery environment.

Desirable

- Experience using Microsoft Fabric for data preparation, notebooks, model execution,

orchestration or analytical workloads.





- Experience using Azure ML, for experiment tracking, model pipelines, deployment or monitoring.
- Experience with other Azure data, analytics or compute services.
- Experience with optimisation libraries such as CVXPY, Pyomo or comparable tools.
- Experience with performance profiling, parallel processing or efficient numerical computation.
- Familiarity with cloud data platforms, lakehouse environments and notebook based development.
- Exposure to MLOps, DevOps and model governance practices.
- Experience developing visualisations and decision support outputs.
- Experience working within consulting or professional services environments.
- Previous modelling experience in areas such as pricing, risk, finance, energy, supply chain, demand forecasting or operations research.
- Relevant Microsoft, data science or analytics certifications.

Personal Attributes

- Curious and eager to learn new technologies and business domains.
- Strong communication and stakeholder engagement skills.
- Collaborative and comfortable working as part of a multidisciplinary team.
- Proactive, adaptable and focused on practical solutions.
- Qualified and comfortable working directly with clients.
- Committed to quality, transparency and continuous improvement.




- Able to ask for guidance while taking ownership of assigned work.

Key Responsibilities

- Gather and interpret business, data and modelling requirements.
- Translate business questions into defined modelling problems, assumptions, inputs,

outputs and acceptance criteria.

- Interpret research, methodology documents, existing models and benchmark results.
- Build statistical, time series, simulation, scenario and optimisation models.
- Apply models to new datasets, assumptions and business scenarios.
- Calibrate model outputs against available benchmarks and client goals
- Refine and optimise models against client constraints.
- Conduct back testing, sensitivity analysis, stress testing and uncertainty analysis.
- Prepare model ready datasets and identify data quality requirements and limitations.
- Write clear, modular and tested Python and SQL.
- Implement reproducible model runs, parameter controls, versioning and logging.
- Work with data and platform engineers to operationalise analytical models.
- Translate technical findings into clear business insights and recommendations.
- Prepare model documentation, technical designs and operating guidance.
- Contribute to reusable analytical frameworks, standards and delivery practices within the company

Preferred Qualifications A degree in Data Science, Statistics, Mathematics, Econometrics, Operations Research,

Engineering, Computer Science or a related discipline is useful. Equivalent practical experience will also be considered.

Relevant Microsoft or analytics certifications are advantageous.

📌 Data Scientist (Sydney)
🏢 BI u0026amp; DW Australia
📍 Sydney