JOBS : Urgent Hiring for Senior Data Science & ML Ops Specialist at Bangalore Location with leading Big4.
About the Role :
We are seeking a mature and self-driven Senior Data Science & ML Ops Specialist to independently lead and anchor AI / ML projects end-to-end. The ideal candidate brings 6-8 years of experience with a solid foundation in Data Science and a proven track record in deploying scalable ML solutions. This role is critical in integrating AI capabilities within business centric applications and ensuring seamless model deployment, monitoring, and scalability through robust ML Ops practices.
We are especially interested in professionals from Bangalore based Global Capability Centers (GCCs) with exposure to Financial Analytics (FP&A) functions and strong delivery experience in Data Science and ML Ops.
Job Title : Senior Data Science & ML Ops Specialist
Location : Bangalore, India
Experience : 68 Years
Industry : Consulting
Domain : Financial Analytics / FP&A
Key Responsibilities :
- Lead end-to-end development and deployment of machine learning solutions across various financial and operational domains.
- Build, deploy, and maintain scalable ML pipelines using industry-standard ML Ops practices.
- Integrate AI / ML solutions into production-grade web applications with a focus on performance, scalability, and maintainability.
- Collaborate cross-functionally with data engineering, product, and business teams to define and deliver impactful analytical solutions.
- Drive automation in model lifecycle management including training, versioning, monitoring, and retraining workflows.
- Ensure adherence to best practices in data security, governance, and compliance in ML deployments.
- Take ownership of project delivery, including timeline management, stakeholder communication, and technical documentation.
Preferred Qualifications & Experience :
6 - 8 years of relevant work experience in Data Science and ML Ops.Strong experience working in or with Financial Analytics (FP&A) teams, preferably within GCCs in Bangalore.Hands-on expertise in Python, ML frameworks (e.g., Scikit-learn, TensorFlow, PyTorch), and ML Ops tools (e.g., MLflow, Kubeflow, SageMaker, or Azure ML).Proficiency in deploying ML models into production environments and managing lifecycle automation.Experience with cloud platforms (AWS, Azure, or GCP) and containerization tools like Docker and Kubernetes.Solid understanding of software development best practices, CI / CD pipelines, and API integrations.Strong communication skills and ability to manage projects with minimal supervision.(ref : hirist.tech)