Data Science Lead
Company Description Brillio is an Enterprise AI Accelerator that helps organizations advance toward agentic enterprises by building AI systems aligned with real-world business operations. For over a decade, Brillio has partnered with leading global enterprises, solving complex engineering challenges with a product engineering mindset. Leveraging its AI accelerator platform, ADAM, Brillio delivers full-stack AI solutions across infrastructure, data, ontology, governance, agents, and applications, all grounded in industry context and human-in-control design. The company emphasizes outcome-based economics and operates from 14 delivery locations across North America, Europe, and Asia. Brillio’s team of more than 5,500 professionals is focused on customer success and innovation in AI-driven transformation.
Role Description The Data Science Lead is a full-time, hybrid role based in Gurugram, with flexibility for part-time work from home. This role is responsible for leading end-to-end data science initiatives, from problem definition and data exploration to model development, validation, and deployment in production environments. The individual will oversee the design of analytical frameworks, guide the selection of statistical and machine learning techniques, and ensure that solutions are scalable and aligned with business objectives. Day-to-day responsibilities include collaborating with cross-functional stakeholders, mentoring data scientists and analysts, reviewing project deliverables, and presenting insights and recommendations to business and technical leaders. The Data Science Lead will also contribute to best practices, standards, and reusable assets that enhance Brillio’s AI accelerator capabilities.
Qualifications
- Candidates should possess strong Data Science and Data Analytics skills for building, evaluating, and deploying predictive and prescriptive models.
- Candidates should possess solid Data Analysis and Analytical Skills to interpret complex datasets, generate insights, and support data-driven decision-making.
- Candidates should possess a robust foundation in Statistics to design experiments, validate model performance, and ensure methodological rigor.
- Experience with Python or R, SQL, and common machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch) is highly beneficial.
- Background in cloud platforms (such as AWS, Azure, or GCP) and MLOps practices for model deployment and monitoring is advantageous.
- Proven experience leading data science teams or projects, with strong stakeholder management and communication skills.
- Master’s or bachelor’s degree in a quantitative field (e.g., Computer Science, Data Science, Statistics, Mathematics, Engineering) or equivalent practical experience.
- Experience in enterprise AI, large-scale data environments, or product-centric engineering organizations is a plus.