Principal Data Scientist

Tiger Analytics is looking for experienced Data Scientists to join our fast-growing advanced analytics consulting firm. Our consultants bring deep expertise in Data Science, Machine Learning and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner. We are looking for top-notch talent as we continue to build the best global analytics consulting team in the world.

As a Principal Data Scientist you will be at the forefront of solving high-impact business problems using advanced machine learning, data engineering, and analytics solutions. The role demands a balanced mix of technical expertise, stakeholder management, and leadership. You will collaborate with cross-functional teams and business partners to define the technical problem statement and hypotheses to test. You will develop efficient and accurate analytical models which mimic business decisions and incorporate those models into analytical data products and tools. You will have the opportunity to drive current and future strategy by leveraging your analytical skills as you ensure business value and communicate the results.

Key Responsibilities

• Lead and contribute to developing sophisticated machine learning models, predictive analytics, and statistical analyses to solve complex business problems.
• Demonstrate proficiency in programming languages such as Python, with the ability to write clean, efficient, and maintainable code.
• Use your robust problem-solving skills to develop data-driven solutions, analyse complex datasets and derive actionable insights that lead to impactful outcomes.
• Take ownership of end-to-end model development—from problem definition and data exploration to model training, validation, deployment, and monitoring—delivering scalable solutions in real world settings.
• Work closely with clients to understand their business objectives, identify opportunities for analytics-driven solutions, and communicate findings clearly and promptly.
• Collaborate with cross-functional teams, including data engineers, software developers, and business stakeholders, to integrate machine learning solutions into business processes, with an emphasis on production-grade deployment.