About the role

The role involves designing, developing, and deploying machine learning models that drive data-driven
decision-making across the organization s products and platforms. The candidate will focus on building robust,
scalable ML solutions for prediction, classification, and optimization use cases. The role requires a
blend of technical expertise, analytical thinking, and business understanding to deliver measurable
impact.

Roles & Responsibilities

- Design, develop, train, test, and deploy end-to-end ML models for production-scale applications.R
- Work across the entire ML lifecycle from data ingestion and feature engineering to model training, evaluation, and deployment.
- Build and maintain MLOps pipelines for scalable and reliable model deployment in real-time environments.
- Collaborate with cross-functional teams (engineering, product, and data) to translate business problems into ML solutions.
- Evaluate and optimize model performance, ensuring efficiency, scalability, and robustness.
- Stay updated on the latest trends in AI/ML, model monitoring, and automation frameworks.
- Experience in building real-time inference pipelines and production-grade ML systems.
- Proficiency in Python and common ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
- Familiarity with cloud platforms (AWS, Azure,



or GCP) and CI/CD for ML.
- Excellent analytical and problem-solving skills with attention to scalability and performance.

Educational Qualification

- Bachelor s or Master s degree in Computer Science, Data Science, Statistics, or a related field from a Tier 1 institution.

- Minimum 5 8 years of hands-on experience in machine learning and data science.
- Robust expertise in Python, SQL, and machine learning libraries such as Scikit-learn,
TensorFlow, and PyTorch.
- Experience with data processing tools (Pandas, NumPy, Spark) and visualization frameworks
(Tableau, Power BI, or Matplotlib).
- Deep understanding of model evaluation metrics, statistical inference, and feature
engineering.
- Hands-on experience with MLOps frameworks (MLflow, Kubeflow, Airflow, Docker, CI/CD
pipelines).
- Familiarity with cloud platforms such as AWS, Azure, or GCP for model deployment.
- Exposure to NLP, computer vision, or time-series models is an added advantage.

Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Senior Data Scientist ML (Hyderabad)
🏢 Rinalytics Advisors
📍 Hyderabad