Senior Manager
- Gurgaon, Haryana
- Permanent
- Full-time
Experience Level: 3–5 Years
Employment Type: Full-TimeJob SummaryWe are seeking a highly skilled and motivated Data Science & MLOps Engineer to join our growing team. This role combines expertise in traditional machine learning, model deployment, and scalable MLOps infrastructure to drive business impact in the Retail and Consumer Packaged Goods (CPG) domain. The ideal candidate will have a strong background in building and operationalizing end-to-end ML pipelines and delivering production-grade ML solutions aligned with real-world use cases such as demand forecasting, pricing optimization, churn prediction, and personalization.Key Responsibilities
- Design and develop robust machine learning models for a variety of use cases including prediction, classification, segmentation, and time-series forecasting.
- Build, automate, and monitor ML pipelines using tools such as MLflow, Kubeflow, Airflow, Metaflow, or equivalent.
- Collaborate with data engineers and domain experts to transform business problems into scalable ML solutions.
- Deploy models into production environments using containerization (Docker) and orchestration frameworks (Kubernetes, AWS SageMaker, Azure ML, GCP Vertex AI).
- Set up and maintain model versioning, tracking, retraining workflows, and CI/CD pipelines for ML systems.
- Implement testing, monitoring, and performance tracking for deployed models to ensure reliability, fairness, and accuracy.
- Collaborate with DevOps and platform teams to improve infrastructure, scalability, and cost-efficiency of ML workloads.
- Stay updated on developments in MLOps, traditional ML algorithms, and best practices for production machine learning.
- Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or a related field.
- 3+ years of experience in developing, deploying, and maintaining ML models in production environments.
- Strong programming skills in Python,SQL, with experience using libraries such as scikit-learn, pandas, NumPy, XGBoost, LightGBM, Prophet, or TensorFlow/PyTorch for traditional ML.
- Hands-on experience with ML workflow tools like MLflow, Kubeflow Pipelines, Airflow, or SageMaker Pipelines.
- Experience deploying ML models using Docker, Kubernetes, or cloud-native ML platforms (AWS, Azure, GCP).
- Good understanding of EDA, model evaluation techniques, feature engineering, and data preprocessing for structured and semi-structured data.
- Proven ability to align ML solutions with retail/CPG business requirements and deliver measurable outcomes.
- Experience with time-series modeling, prophet, recommendation systems, or demand forecasting in retail or CPG.
- Familiarity with model drift detection, automated retraining, and data versioning using tools like DVC or LakeFS.
- Exposure to CI/CD practices for ML, including testing, rollback strategies, and reproducibility.
- Be at the forefront of innovation by applying GenAI to real-world retail/CPG challenges.
- Work in a dynamic, fast-paced, and intellectually stimulating environment.
- Collaborate with cross-functional teams across data science, engineering, and domain consulting.
- Enjoy flexibility through a hybrid/remote work model and opportunities for continuous learning.