Tiger Analytics - Principal Data Scientist - Generative AI

Curious about the role?

We are seeking a deeply technical, hands-on, and strategic Data Science Leader to spearhead complex predictive modeling, advanced analytics, and Generative AI initiatives. The ideal candidate must have a strong foundational background as an expert Data Science practitioner who has evolved into a technical leader and architect.

While rooted heavily in classical Machine Learning and statistical modelling, you will be responsible for integrating modern Generative AI capabilities into the core data science toolkit to solve high-impact enterprise challenges. In this role, you will lead high-performing teams to architect, deploy, and operationalize robust, production-grade models on the cloud.

What your typical day would look like?

- End-to-End Modeling: Lead the full data science lifecycle from sourcing, cleaning, and engineering complex, unstructured data to developing highly scalable models.

- Core Algorithmic Design: Evaluate, select, and build appropriate Machine Learning and Deep Learning algorithms to ensure model accuracy, optimization, and statistical robustness.

- GenAI & LLM Workflows: Apply recent advancements in Large Language Models (LLMs) and Generative AI frameworks (e.g., LangChain, LlamaIndex) to augment traditional analytics.

- Advanced Architecture: Design and optimize Retrieval-Augmented Generation (RAG) systems, prompt engineering frameworks, and specific fine-tuning pipelines to enhance product features and automation.

- Production-Scale Infrastructure: Architect end-to-end ML production setups, covering robust data ingestion, model training pipelines, and real-time monitoring.

- DS Ops & MLOps Governance: Implement strict governance frameworks and best practices for Data Science Operations (DS Ops). Guide teams on model operationalization, version control, performance tuning, and automated retraining pipelines across major cloud platforms (Azure, AWS, GCP, or Snowflake).

- Team Scaling & Mentorship: Manage, mentor, and foster technical growth across high-performing, cross-functional teams of data scientists, ML engineers, and analysts.

- Executive Advisory: Translate highly complex algorithmic outcomes and technical findings into clear, value-driven insights and strategic roadmaps for senior enterprise stakeholders.

Who do we expect?

- 12+ years of total experience in data science, predictive modeling, or advanced analytics, having spent significant time as a hands-on, code-writing practitioner.

- 5+ years of proven leadership experience managing, growing, and guiding specialized data science teams on production-level projects.

- Masters or PhD in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a highly quantitative field (preferred).

Core Technical Toolkit:

- Advanced Data Science: Deep expertise in Machine Learning, Deep Learning, Python, TensorFlow, PyTorch, and classical statistical modeling.

- Modern AI Extensions: Demonstrated proficiency in LLMs, RAG architectures, and fine-tuning pipelines.

- Engineering & Scale: Strong hands-on experience with cloud platforms (AWS, Azure, GCP, or Snowflake), data pipelining, and operationalizing models via MLOps.

Domain Track Record:

- Prior success in executing production projects within highly analytical sectors such as Pharma and BFS.