Senior Data Scientist - GenAI / RAG

Design, develop, and deploy machine learning models to solve complex business problems. Apply statistical analysis, predictive modeling, and advanced analytical techniques to generate actionable insights from large and complex datasets. Develop and implement Generative AI solutions using technologies such as LLMs, Retrieval-Augmented Generation (RAG), and Agentic AI workflows. Evaluate and optimize machine learning and GenAI models for performance, scalability, accuracy, and business impact. Analyze datasets to identify trends, patterns, opportunities, and potential risks. Collaborate with product managers, software engineers, data engineers, and other stakeholders to integrate data science and AI solutions into products and platforms. Translate business requirements into data science and machine learning solutions. Communicate technical findings, model results, and recommendations clearly to technical and non-technical audiences. Participate in customer-facing discussions and confidently explain data science and AI concepts, solutions, and outcomes. Stay current with emerging developments in Machine Learning, Generative AI, LLMs, and related technologies. Provide mentorship and technical guidance to junior data scientists and contribute to a culture of knowledge sharing and continuous learning.

We are looking for a Senior Data Scientist with a strong traditional Machine Learning/Data Science background and hands-on experience in Generative AI, including LLMs, RAG, and Agentic workflows. The ideal candidate will have practical experience designing and implementing data science and AI solutions in real-world environments. This is not a purely academic or junior role. We are looking for someone who can translate business problems into scalable technical solutions, work effectively with cross-functional teams, and confidently communicate with both technical and non-technical stakeholders. Strong communication skills, product-facing experience, and the ability to understand and solve complex business problems are essential., Strong hands-on experience in Data Science and traditional Machine Learning. Practical experience developing and deploying predictive and machine learning models. Hands-on experience with Generative AI, LLMs, RAG architectures, and/or Agentic workflows. Strong proficiency in Python; experience with R is a plus. Experience with machine learning frameworks and libraries such as Scikit-learn, TensorFlow, or PyTorch. Solid understanding of statistical analysis, machine learning algorithms, feature engineering, model evaluation, and data modeling. Strong SQL skills and experience working with databases and large datasets. Experience with big data technologies such as Spark, Hadoop, or similar platforms. Experience with data visualization tools such as Tableau, Power BI, or similar platforms. Understanding of cloud platforms such as AWS, Azure, or Google Cloud. Strong analytical and problem-solving skills. Excellent verbal and written communication skills. Ability to work effectively with technical and non-technical stakeholders. Experience working in product-focused or customer-facing environments. Preferred Qualifications Master’s or Ph.D. in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative discipline. Experience building and deploying enterprise-scale Machine Learning or AI solutions. Experience with modern GenAI frameworks, LLM orchestration, prompt engineering, vector databases, embeddings, and AI application development. Experience designing production-grade RAG or Agentic AI systems. Experience working in technology, software, or enterprise environments. Demonstrated ability to mentor other data scientists and contribute to technical strategy. Experience engaging directly with customers or business stakeholders to understand requirements and present technical solutions. What Success Looks Like Success in this role requires a combination of strong Machine Learning fundamentals, practical Data Science experience, modern GenAI expertise, and excellent communication skills. The successful candidate will be able to move comfortably between business requirements and technical implementation, build solutions that deliver measurable value, and confidently collaborate with product, engineering, and customer-facing teams.