Associate - AI Data Solutions Engineer

Job Purpose

A senior role combines technical depth with consultancy excellence. Seniors act as trusted advisors, clearly articulating business use cases for a wide range of data science and advanced analytics techniques—including predictive modelling, optimisation, and LLM-driven solutions. They lead end-to-end delivery, ensuring solutions are not only technically robust but demonstrably impactful, linking outputs to measurable business outcomes and financial value. Seniors mentor peers, contribute to internal capability building, and influence strategic decisions through clear communication.

Desired Skills And Experience

• Programming: Python for data science; competence in both is advantageous. Skilled in SQL for analysis (DBA experience beneficial).
• Techniques & Tools: Hands-on experience with a broad range of data science methods—visualization, package development, machine learning algorithms, NLP, Shiny/Dash, optimization, deep learning, and reinforcement learning.
• Business Impact: extensive experience in articulating business use cases and deliver practical solutions in at least two technical domains for business and technical audiences.
• Version Control: Regular use of git for code management.
• Deployment: Experience deploying ML models, APIs, and dashboards; exposure to cloud platforms (Azure ML preferred, MSFT certification a plus).
• MLOps: Familiarity with CI/CD, feature stores, drift monitoring, MLflow, DVC, Docker, and Kubernetes is desirable.
• Software Engineering: Exposure to software development, algorithm design, and data architecture is beneficial.
• API & Big Data: Experience with API design/deployment, big data technologies (e.g., Spark), and NoSQL databases is an advantage.
  
  

Key Responsibilities

As a Senior Data Scientist, you will design and implement scalable ML and LLM solutions, optimizing pipelines for ingestion, training, deployment, and monitoring. Apply best practices in governance, testing, and security while driving adoption of modern tools (e.g., Azure ML, Databricks, Hugging Face). Stay ahead of emerging techniques such as prompt engineering and retrieval-augmented generation. Above all, relentlessly measure and communicate business impact, ensuring every solution delivers tangible value from inception to scale-up.

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