Senior Manager

Job Title: Data Science/MLOps Engineer- Retail/CPG Domain

Location:

Job Summary

Data Science & MLOps Engineer

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.

Required Qualifications

• 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.

Preferred Qualifications

• 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.

Why Join Us

• 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.