Data Scientist/Ml Engineer

Role Overview: You will be joining FenixCommerce, an AI-powered shipping intelligence platform based in Noida, India. As a Data Scientist / Machine Learning Engineer, your primary responsibility will be to design, develop, and maintain data pipelines, train and evaluate ML models, and deploy them to production to improve delivery predictions and carrier decisions for major retailers. Key Responsibilities: - Build ML data pipelines: Design, develop, and maintain robust, scalable pipelines for processing shipping and order data. - Train and evaluate models: Develop, train, tune, and validate ML models for delivery-date estimation, transit-time forecasting, and carrier performance. - Deploy to production: Take models from development to production with proper versioning, monitoring, retraining, and performance tracking. - Engineer features: Work with real-world logistics data to engineer features that enhance model accuracy. - Partner across the organization: Collaborate with backend engineers, product teams, and operations to implement model outputs into product capabilities. - Measure impact: Define metrics, conduct experiments, and enhance model quality based on real business outcomes. Qualifications Required: - Experience: Strong hands-on background in data science and ML engineering with a focus on building data pipelines and training production models. - Programming: Proficiency in Python and SQL for writing clean, production-quality code. - ML frameworks: Practical experience with frameworks like scikit-learn, XGBoost, PyTorch, or TensorFlow. - Data engineering: Experience with large-scale data tools such as Spark, Kafka, Airflow, in cloud environments. - Cloud & MLOps: Familiarity with AWS services (SageMaker, S3) and modern MLOps practices for model deployment and monitoring. - Problem-solving: Ability to reason about model trade-offs in a production context with a metrics-driven mindset. Additional Company Details: FenixCommerce specializes in helping e-commerce brands leverage AI-powered shipping intelligence to gain a competitive advantage. Their products focus on accurate estimated delivery dates, carrier optimization, and AI-driven operations tooling operating at an enterprise scale. Note: The application process requires interested candidates to send their profiles/resumes to akhilesh@fenixcommerce.com and nivedita.nandini@fenixcommerce.com for consideration. Role Overview: You will be joining FenixCommerce, an AI-powered shipping intelligence platform based in Noida, India. As a Data Scientist / Machine Learning Engineer, your primary responsibility will be to design, develop, and maintain data pipelines, train and evaluate ML models, and deploy them to production to improve delivery predictions and carrier decisions for major retailers. Key Responsibilities: - Build ML data pipelines: Design, develop, and maintain robust, scalable pipelines for processing shipping and order data. - Train and evaluate models: Develop, train, tune, and validate ML models for delivery-date estimation, transit-time forecasting, and carrier performance. - Deploy to production: Take models from development to production with proper versioning, monitoring, retraining, and performance tracking. - Engineer features: Work with real-world logistics data to engineer features that enhance model accuracy. - Partner across the organization: Collaborate with backend engineers, product teams, and operations to implement model outputs into product capabilities. - Measure impact: Define metrics, conduct experiments, and enhance model quality based on real business outcomes. Qualifications Required: - Experience: Strong hands-on background in data science and ML engineering with a focus on building data pipelines and training production models. - Programming: Proficiency in Python and SQL for writing clean, production-quality code. - ML frameworks: Practical experience with frameworks like scikit-learn, XGBoost, PyTorch, or TensorFlow. - Data engineering: Experience with large-scale data tools such as Spark, Kafka, Airflow, in cloud environments. - Cloud & MLOps: Familiarity with AWS services (SageMaker, S3) and modern MLOps practices for model deployment and monitoring. - Problem-solving: Ability to reason about model trade-offs in a production context with a metrics-driven mindset. Additional Company Details: FenixCommerce specializes in helping e-commerce brands leverage AI-powered shipping intelligence to gain a competitive advantage. Their products focus on accurate estimated delivery dates, carrier optimization, and AI-driven operations tooling operating at an enterprise scale. Note: The application process requires interested candidates to send their profiles/resumes to akhilesh@fenixcommerce.com and nivedita.nandini@fenixcommerce.com for consideration.