Develop and deploy machine learning, statistical, and Generative AI solutions that address business challenges. Responsibilities include analyzing structured and unstructured data, building predictive models, creating RAG and agentic systems, developing data pipelines, managing model lifecycles, ensuring data governance and responsible AI, and communicating insights to technical and business stakeholders.
We are seeking a highly motivated Data Scientist with expertise in machine learning, advanced analytics, and Generative AI technologies. The successful candidate will work with business stakeholders to translate complex business challenges into data-driven solutions and develop scalable analytical products that create measurable business impact.
This role requires strong analytical thinking, hands-on technical expertise, and the ability to stay current with evolving data science and AI technologies. The position is an individual contributor role with significant collaboration across business and technical teams.
Key Roles and Responsibilities of Position:
- Partner with business stakeholders to understand challenges, define analytical approaches, and translate business requirements into data science solutions.
- Analyze structured and unstructured datasets to identify trends, patterns, and actionable insights.
- Design, build, validate, and deploy machine learning and statistical models to support business decision-making.
- Develop Generative AI solutions leveraging prompt engineering, Retrieval-Augmented Generation (RAG), embeddings, and large language models.
- Build and deploy autonomous and multi-agent systems using frameworks such as Google Agent Development Kit (ADK), LangGraph, LangChain, or similar orchestration platforms.
- Design scalable data preparation, feature engineering, and data augmentation pipelines to improve model performance and robustness.
- Support end-to-end model lifecycle activities, including problem scoping, experimentation, model training, evaluation, deployment, monitoring, and optimisation.
- Ensure data quality, integrity, governance, and responsible AI practices throughout the analytics lifecycle.
- Stay informed about emerging developments in machine learning, data science, Generative AI, and analytics methodologies, and apply relevant innovations to business challenges.
- Communicate analytical findings and recommendations effectively to technical and non-technical audiences.
Qualifications
Minimum Qualifications
- Bachelor's degree in Data Science, Computer Science, Statistics, Applied Mathematics, Operations Research, Engineering, or a related quantitative discipline.
- 4+ years of hands-on experience applying machine learning techniques including regression, classification, clustering, decision trees, random forests, and support vector machines.
- 4+ years of experience using Python for data analysis, machine learning, and visualisation, including libraries such as Pandas, NumPy, Matplotlib, and Seaborn.
- 4+ years of experience in SQL and relational database systems.
- 2+ years of experience developing Generative AI applications using prompt engineering, RAG architectures, embeddings, or model fine-tuning techniques.
- Experience developing AI agents or agentic workflows using Google ADK, LangGraph, LangChain, or similar frameworks.
- Familiarity with vector databases and semantic retrieval platforms such as FAISS, Pinecone, Vertex AI Vector Search, or similar technologies.
Preferred Qualifications
- Master's degree or PhD in Computer Science, Statistics, Applied Mathematics, Operations Research, Engineering, or a related quantitative field.
- Experience developing and deploying machine learning solutions on Google Cloud Platform (GCP).
- Strong understanding of probability, statistical inference, linear algebra, numerical methods, optimisation, and neural network fundamentals.
- Excellent problem-solving, communication, stakeholder management, and presentation skills.
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