Data Scientist

You will design, develop, and deploy end‑to‑end analytics, machine learning, and GenAI solutions, working with large‑scale structured and unstructured data. The role balances classical ML and statistical modeling with modern LLM‑based architectures, ensuring both approaches are applied where they create the most value.

• Translate business objectives into well‑defined analytical and mathematical problem statements
• Design, implement, train, tune, validate, and monitor predictive and prescriptive models
• Apply machine learning techniques such as regression, classification, clustering, ensemble methods, deep learning, and time‑series analysis
• Analyze large, high‑dimensional datasets to identify patterns, drivers, anomalies, and trends
• Develop scalable analytics pipelines and reusable modelling frameworks suitable for automation and production deployment
• Design and implement Generative AI and LLM‑based solutions, including NLP, NLG, and semantic search
• Build and deploy Retrieval Augmented Generation (RAG) pipelines for enterprise knowledge and data extraction use cases
• Fine‑tune, evaluate, and monitor Large Language Models for business applications
• Apply text preprocessing, tokenization, embedding generation, prompt engineering, and LLM orchestration techniques
• Leverage modern GenAI frameworks and libraries (e.g., Transformers, LangChain‑based workflows) to build scalable AI applications.

• Build end‑to‑end system designs covering data ingestion, feature engineering, modeling, inference, and user‑facing outputs
• Partner with IT and platform teams to deploy analytics and AI solutions on cloud infrastructure
• Work with cross‑functional stakeholders to communicate insights, model results, and recommendations clearly
• Explore new data sources, tools, and technologies to continuously enhance analytics and AI capabilities
• Package findings into clear documentation, dashboards, and executive‑level presentations

• Bachelor’s or Master’s degree in Data Science, Computer Science, Engineering, Statistics, Applied Mathematics, Operations Research, or related quantitative field
• Strong hands‑on experience in Python for data analysis, machine learning, and AI development
• Experience with common ML and analytics libraries (e.g., NumPy, Pandas, scikit‑learn, TensorFlow, PyTorch)
• Experience with SQL and working with relational and large‑scale datasets
• Experience building analytical models and communicating insights to business stakeholders
• Master’s or Ph.D. in a quantitative or engineering discipline
• 5+ years of experience applying machine learning, advanced analytics, and AI techniques in real‑world settings
• Strong experience with Generative AI, LLMs, NLP, and RAG architectures
• Experience with cloud platforms, especially Google Cloud Platform (GCP), including Vertex AI, BigQuery, and related services
• Experience working with large‑scale data ecosystems (e.g., NoSQL databases, Hadoop ecosystem)
• Proficiency with version control (Git) and collaborative development practices
• Strong mathematical foundations in probability, linear algebra, optimization, and numerical methods
• Excellent problem‑solving, communication, and data storytelling skills
• Ability to work independently while collaborating effectively with cross‑functional teams

Comfortable operating in fast‑paced, ambiguous, and evolving problem spaces.