Senior Data Scientist - GenAI / RAG

Senior Data Scientist – Machine Learning & GenAI

About the Role

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.

Key Responsibilities

• 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.

Required Skills & Experience

• 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.