Data Scientist

This is a fully remote position, open to applicants in Australia.

📋 Description

• Create, develop, and validate predictive models (such as lead scoring, propensity modeling, forecasting, and risk prediction) to guide operational and strategic choices.

• Design and implement generative AI solutions, which encompass LLM-powered agents, retrieval-augmented generation (RAG) pipelines, and prompt engineering workflows to streamline and enhance business operations.

• Convert business challenges into well-defined analytical and modeling projects, collaborating with stakeholders to establish success criteria and measurable results.

• Execute feature engineering, model selection, hyperparameter tuning, and thorough evaluations utilizing suitable statistical and machine learning methodologies.

• Transition models into scalable, maintainable pipelines, working alongside data engineering to incorporate outputs into downstream systems (such as CRM, BI tools, and operational dashboards).

• Oversee model performance after deployment, manage model drift, and apply retraining strategies as necessary.

• Present findings and model results to non-technical stakeholders through effective visualizations, written summaries, and presentations.

• Keep abreast of advancements in applied machine learning and generative AI, contributing to the team’s knowledge-sharing and capability-enhancement initiatives.

• Offer guidance, direction, and oversight to one direct report within the Data, Insights & AI team.

⛳️ Requirements

• Bachelor's or Master's degree in data science, statistics, computer science, mathematics, engineering, economics, or a related quantitative discipline.

• 3–5 years of relevant professional experience in a data science, machine learning, or applied analytics capacity.

• Experience managing or mentoring one or more analysts, including providing technical advice, performance feedback, and support for professional growth.

• Strong expertise in Python for data science (pandas, scikit-learn, XGBoost/LightGBM, statsmodels, or similar tools).

• Practical experience in developing and deploying predictive models in a commercial or operational setting (not limited to academic or Kaggle projects).

• Hands-on experience with large language models (LLMs), including prompt engineering, fine-tuning, or creating agentic AI workflows using frameworks such as LangChain, Semantic Kernel, or Azure AI Foundry.

• A solid foundation in statistics and experimental design (including hypothesis testing, regression, classification, and time series analysis).

• Familiarity with MLOps practices: model versioning, CI/CD for machine learning pipelines, monitoring, and ensuring reproducibility.

• Excellent communication skills, with the ability to convey complex technical work to senior business stakeholders clearly and with an outcome-oriented focus.

🏝️ Benefits

• Flexible “Work Anywhere” model (remote, hybrid, or office options).

• High-growth environment with robust career development opportunities.

• Collaborative, innovative, and people-first culture.

• Certified as a Great Place to Work in Australia & Malaysia.

• Professional development support, including access to certifications and training programs.

• Flexible working arrangements (remote, hybrid, or office).

• Employee Assistance Program and well-being initiatives.

• Access to LinkedIn Learning and career development programs.

• IT Equipment provided for your success.