We are seeking a highly skilled Senior Data Scientist with strong MLOps experience to design, build, and operationalize scalable machine learning solutions in an enterprise environment. This role focuses on end-to-end ML lifecycle management, distributed data processing, and production-grade model deployment using AWS SageMaker, Ray, and Dask.

The ideal candidate will have hands-on experience delivering ML solutions for large-scale, data-intensive systems, preferably within banking, payments, or financial services domains.

Key Responsibilities

Data Science & Modeling

Design and develop machine learning models for structured and semi-structured data.
Perform advanced feature engineering, data exploration, and statistical analysis.
Build, evaluate, and optimize models for accuracy, performance, and scalability.
Apply supervised and unsupervised learning techniques for real-world business problems.

Distributed Data Processing

Implement large-scale data preprocessing and feature pipelines using Dask.
Optimize ETL and batch processing workloads on distributed compute environments.
Work with datasets stored in Amazon S3 and cloud-native data platforms.

MLOps & Model Lifecycle

Build and manage end-to-end ML pipelines using Amazon SageMaker Pipelines.
Train and tune models using distributed compute frameworks such as Ray and Ray Tune.
Register, version, and promote models using SageMaker Model Registry.
Deploy models using SageMaker Endpoints, batch inference, or scalable serving solutions.
Implement monitoring for data drift, model performance, and operational health.

Cloud & DevOps Integration

Containerize ML workloads using Docker and manage images in Amazon ECR.
Integrate ML workflows with CI/CD pipelines (GitHub, CodeBuild, Jenkins, etc.).
Collaborate with platform, DevOps, and security teams to meet enterprise standards.
Ensure compliance with data security, access control (IAM), and audit requirements.

Collaboration & Leadership

Work closely with product owners, data engineers, and software engineers.
Translate business requirements into scalable ML solutions.
Provide technical guidance and mentorship to junior data scientists.
Participate in architecture and design reviews.

Required Qualifications

Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field.
6+ years of experience in data science or machine learning roles.
Strong proficiency in Python (Pandas, NumPy, Scikit-learn).
Hands-on experience with Amazon SageMaker (training, pipelines, deployment).
Experience with Ray for distributed training and hyperparameter tuning.
Experience using Dask for large-scale data processing and feature engineering.
Solid understanding of ML model evaluation, validation, and optimization techniques.
Experience deploying ML models into production environments.
Strong understanding of cloud-native ML architectures on AWS.

Preferred Qualifications

- Experience in banking, payments, fraud detection, or financial services domains.
- Familiarity with PyTorch, TensorFlow, or XGBoost in distributed settings.
- Experience with real-time or near real-time inference systems.
- Knowledge of data drift detection, model monitoring, and retraining strategies.
- Exposure to regulatory and compliance requirements in financial systems.
- Prior experience working in Agile/Scrum teams.

Data Engineer / MLOps Specialist (W2-Only) | 4-8 year of Exp

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