Data Scientist Principals #IN1291

About this role

Lead advanced analytics initiatives to solve complex business problems using quantitative analysis, statistical modeling, and machine learning techniques. Design, develop, and deploy machine learning models to support decision systems, recommendation and next best action frameworks, anomaly detection, pattern recognition, and forecasting/time series analysis. Liaise with business stakeholders and leverage knowledge to industrialize and monetize insights from advanced analytics projects. Research, implement, and validate advanced analytical and algorithmic methods, including causal inference, impact measurement, predictive modeling, clustering, and optimization techniques, to quantify business outcomes and evaluate the effectiveness of data driven interventions. Apply statistical inference and data mining techniques to analyze large, diverse datasets and identify meaningful relationships, trends, and drivers of performance. Develop and support scalable, end to end analytics and machine learning solutions, including data processing pipelines and model lifecycle management practices, using commonly used programming languages and distributed data processing frameworks. Support both batch and near real time/streaming data processing in a cloud based analytics environment. Design and maintain modern data platform architectures, including lakehouse style data architectures, with responsibilities for data quality, schema management, and governance controls. Utilize sampling methods and experimental or quasi experimental approaches as needed to support research, analysis, and decision making. Translate analytical results and model insights into clear business language, communicate findings to technical and non technical stakeholders, and partner with domain experts to ensure alignment with business objectives. Lead requirements analysis, assess interdependencies and priorities, and align analytics initiatives with strategic roadmaps and organizational goals. Apply Agile software development practices to design, develop, and enhance analytics solutions. Provide technical leadership and mentorship to less experienced engineers or analysts on data science methodologies, machine learning solution design, and best practices for scalable analytics systems.
Originally posted on Himalayas