Senior Data Scientist, Ahamove

Job description

• Design, develop, and deploy machine learning models for recommendation systems, personalization, forecasting, ranking, and user behavior analysis.
• Build and optimize end-to-end AI pipelines from data ingestion, feature engineering, model training, evaluation, deployment, and monitoring.
• Train and fine-tune machine learning models on cloud platforms for large-scale predictive modeling tasks.
• Work with large-scale datasets using Databricks, Spark, and distributed data processing frameworks.
• Develop LLM-powered applications using modern frameworks such as LangChain, tool calling, RAG pipelines, vector databases, and agent workflows.
• Work with structured and unstructured datasets including transactional data, logs, documents, and user interaction data.
• Collaborate with product managers and engineering teams to translate business problems into scalable AI solutions.
• Optimize model inference, retrieval performance, latency, and production reliability.
• Conduct experimentation, A/B testing, and performance analysis to improve business KPIs.
• Design and implement robust model evaluation frameworks including offline evaluation, online A/B testing, and business KPI measurement.
• Monitor model drift, prediction quality, and production performance to ensure long-term model reliability.
• Stay up to date with advances in machine learning, generative AI, recommendation systems, and MLOps technologies.

Job requirement

• 4+ years of experience in Data Science, Machine Learning, or AI Engineering roles.
• Strong proficiency in Python and SQL.
• Experience training and deploying machine learning models on cloud platforms such as AWS, GCP, or Azure.
• Experience working with Databricks, Spark, or distributed computing environments for large-scale data processing.
• Strong understanding of recommendation systems, ranking models, clustering, classification, and predictive modeling.
• Experience working with LLM applications, RAG systems, vector databases, embeddings, and prompt engineering.
• Experience evaluating predictive models, recommendation systems, ranking systems and LLM applications using both offline and online metrics.
• Familiarity with LangChain, LangGraph, or similar GenAI frameworks.
• Experience with cloud platforms such as AWS, GCP, or Azure.
• Experience with containerization and orchestration tools such as Docker and Kubernetes is a plus.
• Strong analytical thinking and ability to work cross-functionally in a fast-paced environment.
• Good communication skills in both technical and business contexts.

Benefit

• Physical Wellbeing Benefit: General Insurance, Medical check-up, Accident Insurance, Healthcare Insurance.
• Emotional Wellbeing Benefit: Company Trip, Year End Party, Aha Hour Activities, Special Day Gifts, Aha Club (Badminton, Soccer).
• Financial Wellbeing Benefit: Grab/Be For Work (Tech/Lead Level), Workplace Relocation, 13th Month Salary, PP Appreciate, Annual Leave Remain.