Data Scientist

This is an opportunity to work on complex, real-world problems in a fast-paced, collaborative environment.\n\nKey Responsibilities\nEnd-to-End Project Leadership: Take ownership of data science projects, from initial research and experimentation to scalable deployment, monitoring, and ongoing optimization.\nModel Development: Design, build, and optimize advanced statistical and machine learning models to solve complex business problems.\nLLM & Generative AI: Research and implement innovative LLM/GenAI solutions, including advanced prompt engineering, Retrieval-Augmented Generation (RAG) frameworks, and parameter-efficient fine-tuning for specific business needs.\nCross-functional Collaboration: Partner with product, engineering, and business stakeholders to translate complex challenges into actionable data science solutions and communicate findings effectively to technical and non-technical audiences.\nMentorship: Guide and mentor junior data scientists and analysts, fostering a culture of technical excellence and continuous learning.\nInfrastructure & Deployment: Work with data engineering teams to design scalable data models and robust, automated ML pipelines using MLOps best practices and cloud services (AWS/GCP/Azure).\nPerformance Monitoring: Implement monitoring frameworks to track and enhance the performance of deployed models.\n\nCore Technical Skills\nMachine Learning & Statistics:\nStrong foundation in supervised & unsupervised ML algorithms, evaluation metrics, and feature engineering.\nPractical experience with hands-on modelling and experimentation.\nLLM & Generative AI:\nConceptual and practical understanding of LLM architecture, prompt engineering, RAG, parameter fine-tuning, and LLM evaluation/monitoring.\nExposure to vector databases\nExecuted at least 1 impactful LLM/GenAI implementation in real-world settings.\nProgramming:\nProficiency in Python, R, and SQL for model development and data analytics.\nDeployment & Automation:\nExperience with cloud platforms (AWS/GCP/Azure)\nAPI integration and orchestration for scalable solutions.\nPractical experience with MLOps tools like MLflow, Docker, or Kubernetes for CI/CD pipelines.\n\nSoft Skills\nStrong analytical thinking and structured problem-solving ability.\nExcellent interpersonal communication and stakeholder engagement.\nAbility to work in a fast-paced, collaborative environment and lead through influence.\nHigh ownership and drive to deliver measurable impact.\nCollaborate with product, engineering, and business stakeholders to translate complex AI solutions into business impact.\n\nEducational Background\nBachelor’s/Master’s degree in Computer Science, Data Science, Statistics, Mathematics\nEquivalent industry experience with strong hands-on contributions will be preferred.\n\nRegards,\nSaba