Data Scientist

Data Scientist

Responsibilities:

• Take the ownership of AI /ML use cases, from design and implementation to continuous enhancement.
• Serve as a technical specialist on AI, with more focus on Gen AI, within a data science team. Design and develop solutions to improve advisors sales journey and optimize operational efficiency.
• Collaborate with other data engineers, analysts, data scientists, product specialists, and other stakeholders to build well-crafted, pragmatic, and robust solutions that meet business requirements.
• Stakeholder management and engagement. Proactively engage with stakeholders to understand their needs and translate them into technical requirements for AI / ML modelling.
• Maintain documentation of dataset curation, modelling approach, model performance, code changes, and workflows.
• Demonstrate a strong understanding of data privacy regulations such as PDPA, and AI governance guidelines to ensure compliance.
• Foster an innovative and growth-oriented mindset, continuously seeking opportunities to enhance AI /ML models and drive improvements across the organization.

Requirements:

• 3~5 years of experience in data science role with relevant experience in AWS platform.
• A bachelor’s degree in computer science or equivalent.
• Familiarity with techniques for Document Chunking, Embedding, Information Retrieval for improving model accuracy and relevance.
• Expertise in Prompt Engineering for designing and managing effective prompts.
• Hands-on experience with AWS Bedrock, including deploying solution using foundation models (e.g., Anthropic Claude, Amazon Titan, Meta Llama) and integrating them into scalable applications using APIs and orchestration tools.
• Experience in designing and applying evaluation frameworks for Gen AI models, including metrics and human-in-the-loop evaluation. Able to implement validation process and guardrails to mitigate risks such as hallucination and misinformation, ensuring the reliability and trustworthiness of AI-generated outputs.
• Experience in Agentic Flow for maximizing the utility of models, including understanding user intent and context to drive meaningful interactions.
• In depth knowledge of supervised and unsupervised ML Models – linear & logistic regression, clustering, tree-based models like random forest, bagging and boosting models. 
• In depth knowledge on feature engineering techniques, hyperparameter tuning and model evaluation. 
• Proficient in SQL, Python, and Spark. 
• Proven experience in implementing MLOps practices on AWS.
• Familiar with Data Warehouses such as Redshift, BigQuery, Snowflake, Hive, and S3.
• Passionate about technology and always looking to upskill based on new developments in AI space.
• AWS certifications will be a plus. 
• Experience in the financial industry, telecommunications, or consulting is preferred.