What are the responsibilities and job description for the Data Science position at Mphasis?
Job Description:
Tachyon Predictive AI team seeking a hybrid Data Science & ML Ops Engineer to drive the full lifecycle of machine learning solutions—from data exploration and model development to scalable deployment and monitoring. This role bridges the gap between data science model development and production-grade ML Ops Engineering.
About the Role
This role involves developing predictive models and maintaining ML pipelines to enhance fraud reduction, operational efficiency, and customer insights.
Responsibilities
- Develop predictive models using structured/unstructured data across 10 business lines, driving fraud reduction, operational efficiency, and customer insights.
- Leverage AutoML tools (e.g., Vertex AI AutoML, H2O Driverless AI) for low-code/no-code model development, documentation automation, and rapid deployment.
- Develop and maintain ML pipelines using tools like MLflow, Kubeflow, or Vertex AI.
- Automate model training, testing, deployment, and monitoring in cloud environments (e.g., GCP, AWS, Azure).
- Implement CI/CD workflows for model lifecycle management, including versioning, monitoring, and retraining.
- Monitor model performance using observability tools and ensure compliance with model governance frameworks (MRM, documentation, explainability).
- Collaborate with engineering teams to provision containerized environments and support model scoring via low-latency APIs.
Qualifications
- Strong proficiency in Python, SQL, and ML libraries (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch).
- Experience with cloud platforms and containerization (Docker, Kubernetes).
- Familiarity with data engineering tools (e.g., Airflow, Spark) and ML Ops frameworks.
- Solid understanding of software engineering principles and DevOps practices.
- Ability to communicate complex technical concepts to non-technical stakeholders.
Required Skills
- Python
- SQL
- ML libraries (scikit-learn, XGBoost, TensorFlow, PyTorch)
- Cloud platforms (GCP, AWS, Azure)
- Containerization (Docker, Kubernetes)
Preferred Skills
- Data engineering tools (Airflow, Spark)
- ML Ops frameworks
- Software engineering principles
- DevOps practices