Data Scientist - Recommendation Engine & AI Innovation

Responsibilities
• Collaborate on designing, implementing, and improving Recommendation Engine systems in production
• Explore and integrate Generative AI (GenAI) and Large Language Models (LLMs) to enhance existing workflows
• Use statistical analysis and machine learning to build scalable solutions for user behavior, content recommendations, and campaign optimization
• Experiment with AI-powered tools (e.g. GitHub Copilot, and ChatGPT) to accelerate coding, debugging, and workflow efficiency
• Validate hypotheses via A/B testing and contribute to data-driven decision making
• Research emerging AI/ML trends (e.g. NLP, and computer vision) to identify business impact opportunities
• Work with engineers to deploy AI-driven systems and learn best practices in MLOps

Requirements
• Bachelor’s degree in Computer Science, Statistics, or related fields
• 0–2 years of experience in ML/AI projects (academic, internships, or personal projects are welcome)
• Strong foundational knowledge of ML algorithms (e.g. regression, clustering, and neural networks) and libraries (scikit-learn, and TensorFlow/PyTorch)
• Proficiency in Python; familiarity with R, Java, or Scala is a bonus
• Eagerness to learn GenAI/LLM applications (e.g. Hugging Face, and LangChain) and AI productivity tools
• Basic understanding of data visualization (Power BI, and Looker) and SQL
• Curiosity to experiment with new technologies and improve existing systems
It’s Great If You Have
• Exposure to cloud platforms (GCP, and AWS), Docker, Kubernetes, or MLOps tools (Airflow, and dbt)
• Experience with LLM fine-tuning, prompt engineering, or open-source AI projects
• Familiarity with graph/vector databases or A/B testing frameworks
• Contributions to GitHub repositories, Kaggle competitions, or AI communities

• Candidates with exceptional aptitude and a portfolio of projects may be considered even if they lack formal experience

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