Data Scientist - MLOps

Job Description

Purpose of the role

To use innovative data analytics and machine learning techniques to extract valuable insights from the bank's data reserves, leveraging these insights to inform strategic decision-making, improve operational efficiency, and drive innovation across the organisation.

Accountabilities

• Identification, collection, extraction of data from various sources, including internal and external sources.
• Performing data cleaning, wrangling, and transformation to ensure its quality and suitability for analysis.
• Development and maintenance of efficient data pipelines for automated data acquisition and processing.
• Design and conduct of statistical and machine learning models to analyse patterns, trends, and relationships in the data.
• Development and implementation of predictive models to forecast future outcomes and identify potential risks and opportunities.
• Collaborate with business stakeholders to seek out opportunities to add value from data through Data Science.

Assistant Vice President Expectations

• To advise and influence decision making, contribute to policy development and take responsibility for operational effectiveness. Collaborate closely with other functions/ business divisions.
• Lead a team performing complex tasks, using well developed professional knowledge and skills to deliver on work that impacts the whole business function. Set objectives and coach employees in pursuit of those objectives, appraisal of performance relative to objectives and determination of reward outcomes
• If the position has leadership responsibilities, People Leaders are expected to demonstrate a clear set of leadership behaviours to create an environment for colleagues to thrive and deliver to a consistently excellent standard. The four LEAD behaviours are: L - Listen and be authentic, E - Energise and inspire, A - Align across the enterprise, D - Develop others.
• OR for an individual contributor, they will lead collaborative assignments and guide team members through structured assignments, identify the need for the inclusion of other areas of specialisation to complete assignments. They will identify new directions for assignments and/ or projects, identifying a combination of cross functional methodologies or practices to meet required outcomes.
• Consult on complex issues; providing advice to People Leaders to support the resolution of escalated issues.
• Identify ways to mitigate risk and developing new policies/procedures in support of the control and governance agenda.
• Take ownership for managing risk and strengthening controls in relation to the work done.
• Perform work that is closely related to that of other areas, which requires understanding of how areas coordinate and contribute to the achievement of the objectives of the organisation sub-function.
• Collaborate with other areas of work, for business aligned support areas to keep up to speed with business activity and the business strategy.
• Engage in complex analysis of data from multiple sources of information, internal and external sources such as procedures and practises (in other areas, teams, companies, etc).to solve problems creatively and effectively.
• Communicate complex information. 'Complex' information could include sensitive information or information that is difficult to communicate because of its content or its audience.
• Influence or convince stakeholders to achieve outcomes.

All colleagues will be expected to demonstrate the Barclays Values of Respect, Integrity, Service, Excellence and Stewardship - our moral compass, helping us do what we believe is right. They will also be expected to demonstrate the Barclays Mindset - to Empower, Challenge and Drive - the operating manual for how we behave.

Join Barclays as a Data Scientist -ML Ops, you will be responsible for designing, building, and operationalizing scalable machine learning solutions using robust MLO ps practices. The candidate will work across the end-to-end ML Ops lifecycle-covering deployment, monitoring, and continuous improvement-ensuring production-grade, governed, and efficient ML systems. This role plays a key part in enabling enterprise-scale AI/ML solutions aligned to Barclays’ cloud and Databricks-based ML Ops framework, ensuring consistency, auditability, and faster time-to-value for business use cases.

To be successful in this role you should have:

• Strong hands-on experience in ML Ops (model lifecycle management, CI/CD, deployment, monitoring).
• Experience in building and operationalizing ML models across environments.
• Understanding of full ML lifecycle including experimentation, training, validation, deployment, and monitoring.
• Strong proficiency in Python and PySpark.
• Experience with large-scale data processing and big data ecosystems.
• Hands-on experience with AWS and/or Databricks platforms.
• Experience with data pipelines, feature stores, and model registries.
• Experience with tools such as:
  • MLflow (experiment tracking, registry)
  • Airflow / orchestration tools
  • Docker / containerization

• Experience with AWS services -S3, IAM, CloudWatch, EMR/Glue, etc.
• Understanding of scalable data platforms (data lakes, data warehouses).
• Experience with model monitoring, drift detection, and performance tracking.
• Understanding of data governance, model governance, and compliance requirements.

Some other highly valued skills include:

• Experience with Databricks-native ML Ops capabilities(Unity Catalog, ML flow registry, asset bundles).
• Exposure to real-time / batch inference pipelines.
• Knowledge of feature store concepts and implementation.
• Familiarity with API integration and model serving frameworks.
• Understanding of DevOps / Infrastructure-as-Code(e.g., Terraform, CloudFormation).

You may be assessed on the key critical skills relevant for success in role, such as risk and controls, change and transformation, business acumen, strategic thinking and digital and technology, as well as job-specific technical skills.

The successful candidate will be based in Pune.