Data Science + Gen AI Leader

Tiger Analytics is a global leader in AI and analytics, helping Fortune 1000 companies solve their toughest challenges. We offer full-stack AI and analytics services & solutions to empower businesses to achieve real outcomes and value at scale. We are on a mission to push the boundaries of what AI and analytics can do to help enterprises navigate uncertainty and move forward decisively. Our purpose is to provide certainty to shape a better tomorrow.

Our team of 4000+ technologists and consultants are based in the US, Canada, the UK, India, Singapore, and Australia, working closely with clients across CPG, Retail, Insurance, BFS, Manufacturing, Life

Sciences, and Healthcare. We are a Great Place to Work-Certified , recognized globally for our dedication to innovation and excellence.

Curious about the role?

We are seeking a deeply technical, hands-on Data Science leader with over 15 years of experience to spearhead complex predictive modeling, advanced analytics, and machine learning initiatives. The ideal

candidate must have a strong foundational background as an expert Data Science practitioner who has evolved into a strategic leader.

While rooted heavily in classical Machine Learning, Deep Learning, and statistical modelling, you will also integrate modern Generative AI capabilities into the core data science toolkit. In this role, you will lead

high-performing teams to architect, deploy, and operationalize robust, production-grade models that solve high-impact enterprise challenges.

What your typical day would look like?

As part of this role, you will:

• End-to-End Modeling: Lead the full data science lifecyclefrom 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 ?

1. 12+ years of total experience in data science, predictive modeling, or advanced analytics, having spent significant time as a hands-on, code-writing practitioners
2. 5+ years of proven leadership experience managing, growing, and guiding specialized data science teams on production-level projects.
3. Masters or PhD in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a highly quantitative field (preferred).
4. Core Technical Toolkit: Advanced Data Science: Deep expertise in Machine Learning, Deep Learning, Python, TensorFlow, PyTorch, and classical statistical modeling.
5. Modern AI Extensions: Demonstrated proficiency in LLMs, RAG architectures, and fine-tuning pipelines.
6. Engineering & Scale: Strong hands-on experience with cloud platforms (AWS, Azure, GCP, or Snowflake), data pipelining, and operationalizing models via MLOps.
7. Domain Track Record: Prior success in executing production projects within highly analytical sectors such as Retail, CPG or Manufacturing is highly desirable.

Note: The designation will be commensurate with expertise and experience. Compensation packages are among the best in the industry.

Additional Benefits: Health insurance (self & family), virtual wellness platform, and knowledge communities.