As a Full-Stack Data Scientist at our growing SaaS and consulting technology company based in Noida, you will be responsible for owning DS/ML components end-to-end. This includes translating product and business needs into analytical tasks, building validated predictive models, and deploying them as reliable services. Your role will involve partnering closely with product, engineering, and platform teams to design scalable and observable workflows, ensuring model correctness and operational readiness. Your success will be defined by delivering actionable, production-ready models that improve business metrics and integrate cleanly into existing systems, with clear documentation and measurable impact. **Key Responsibilities:**
Translate product and business problems into structured analytical tasks, defining success metrics and delivering clear project scopes that drive measurable business impact. - Conduct rigorous exploratory data analysis to identify trends, drivers, anomalies, and root causes, and communicate findings to technical and non-technical stakeholders. - Design, build, and validate predictive models (classification, regression, anomaly detection) with a strong emphasis on feature engineering, bias checks, and robust validation strategies. - Deploy and maintain ML models in production using MLOps best practices: versioning, CI/CD integration, automated testing, monitoring, and retraining pipelines. - Design and implement ML-backed APIs with attention to throughput, latency, pagination, and fault tolerance to support product requirements. - Collaborate with data engineering and platform teams to ensure data quality, lineage, and scalable pipelines that support reliable model performance. **Essential Skills & Technologies:**
Strong hands-on expertise in Python for data science, including libraries such as pandas, scikit-learn, and model-serving frameworks, with production-grade coding practices. - Deep practical experience taking work from EDA and insight generation to predictive modeling and operational use, prioritizing impact and correctness. - Working knowledge of MLOps practices, including model deployment patterns, CI/CD pipelines, model versioning, monitoring, and retraining approaches. - Solid understanding of data structures and algorithms relevant to efficient data processing, API systems, and scalable service design. - Experience developing and integrating with analytics/ML service APIs, and designing for performance and reliability under load. - Proficient SQL skills for analysis, debugging, and data validation, with familiarity with distributed compute concepts. **Additional Plus:**
Exposure to Spark or other distributed data processing frameworks and experience designing solutions for large-scale datasets. - Experience with cloud ML services (AWS SageMaker, GCP AI Platform, Azure ML) and container orchestration for model serving. - Familiarity with monitoring, observability, and alerting tools for model and API performance (Prometheus, Grafana, Sentry). Joining our team means working on end-to-end ML systems that move from exploratory analysis to deployed models powering customer-facing services. You will receive strong support for career growth and technical ownership, enabling you to continuously improve models and operational practices, while focusing on impact, correctness, maintainability, and measurable outcomes. As a Full-Stack Data Scientist at our growing SaaS and consulting technology company based in Noida, you will be responsible for owning DS/ML components end-to-end. This includes translating product and business needs into analytical tasks, building validated predictive models, and deploying them as reliable services. Your role will involve partnering closely with product, engineering, and platform teams to design scalable and observable workflows, ensuring model correctness and operational readiness. Your success will be defined by delivering actionable, production-ready models that improve business metrics and integrate cleanly into existing systems, with clear documentation and measurable impact. **Key Responsibilities:**
Translate product and business problems into structured analytical tasks, defining success metrics and delivering clear project scopes that drive measurable business impact. - Conduct rigorous exploratory data analysis to identify trends, drivers, anomalies, and root causes, and communicate findings to technical and non-technical stakeholders. - Design, build, and validate predictive models (classification, regression, anomaly detection) with a strong emphasis on feature engineering, bias checks, and robust validation strategies. - Deploy and maintain ML models in production using MLOps best practices: versioning, CI/CD integration, automated testing, monitoring, and retraining pipelines. - Design and implement ML-backed APIs with attention to throughput, latency, pagination, and fault tolerance to support product requirements. - Collab