Data Scientist: Responsible AI & Client Impact

Faculty

Sydney

On-site

AUD 90,000 - 130,000

Full time

4 days ago
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Job summary

Faculty is seeking a Data Scientist to collaborate with clients and cross-functional teams to define project scope, ensure technical feasibility, and drive delivery excellence. You will design and deliver bespoke data science solutions, leveraging Python, NumPy, Pandas, and deep-learning frameworks to solve high-impact problems and advance practical AI.

Strong communication with non-technical stakeholders and the ability to translate complex analytics into actionable insights are essential; the

Qualifications

  • Proven experience in a professional data science or quantitative role with strong mathematical/statistical ability.
  • Strong Python programming with NumPy/Pandas and a deep-learning framework (TensorFlow or PyTorch).
  • Solid grasp of core data science techniques (supervised/unsupervised learning, time-series, NLP, model validation).
  • Rigorous scientific mindset and entrepreneurial approach to translating business problems into a mathematical framework.

Responsibilities

  • Map the end-to-end data science approach and design the software architecture for projects.
  • Drive the technical scoping and feasibility assessment of new projects.
  • Build strong client relationships by acting as a technical advisor and shaping engagements.
  • Deliver bespoke algorithms and scalable software solutions meeting high-stakes decision-making standards.
  • Set the technical bar for the project team, ensuring code quality and delivery rigour.
  • Contribute to Faculty's thought leadership through teaching, public speaking, or open-source work.

Skills

Python
NumPy
Pandas
TensorFlow
PyTorch
Statistical modelling

Job description

Faculty is seeking a Data Scientist to collaborate with clients and cross-functional teams to define project scope, ensure technical feasibility, and drive delivery excellence. You will design and deliver bespoke data science solutions, leveraging Python, NumPy, Pandas, and deep-learning frameworks to solve high-impact problems and advance practical AI.

Strong communication with non-technical stakeholders and the ability to translate complex analytics into actionable insights are essential; the

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