Data Scientist

About The Role The role involves designing, building, and scaling high-performance machine learning models and data pipelines that drive core product decisions and automated user experiences. The team works at the intersection of statistics, software engineering, and large-scale data systems to solve complex business problems with measurable impact. Key Responsibilities Design, train, and validate predictive models using Python, scikit-learn, and PyTorch for production environmentsBuild and maintain robust ETL and feature engineering pipelines processing terabytes of structured and unstructured data using SQL and PySparkDeploy machine learning models to cloud infrastructure using containerization tools like Docker and orchestration services on AWS or GCPMonitor deployed models for performance degradation, data drift, and latency bottlenecks, implementing automated remediation where necessaryCollaborate with product and engineering teams to translate business requirements into quantitative technical specifications and deliverable milestones What We Are Looking For 3-6 years of professional experience in data science, quantitative analytics, or machine learning engineeringStrong proficiency in Python, SQL, and core scientific computing libraries such as Pandas, NumPy, and Scikit-LearnDemonstrated experience deploying and maintaining machine learning models in production cloud environments (AWS, GCP, or Azure)Solid understanding of statistical modeling, experimental design, A/B testing, and hypothesis testing methodologiesBonus: Master's or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field; contributions to open-source data science libraries