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.

Analyst Expectations

• To perform prescribed activities in a timely manner and to a high standard consistently driving continuous improvement.
• Requires in-depth technical knowledge and experience in their assigned area of expertise
• Thorough understanding of the underlying principles and concepts within the area of expertise
• They lead and supervise a team, guiding and supporting professional development, allocating work requirements and coordinating team resources.
• 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 develop technical expertise in work area, acting as an advisor where appropriate.
• Will have an impact on the work of related teams within the area.
• Partner with other functions and business areas.
• Takes responsibility for end results of a team’s operational processing and activities.
• Escalate breaches of policies / procedure appropriately.
• Take responsibility for embedding new policies/ procedures adopted due to risk mitigation.
• Advise and influence decision making within own area of expertise.
• Take ownership for managing risk and strengthening controls in relation to the work you own or contribute to. Deliver your work and areas of responsibility in line with relevant rules, regulation and codes of conduct.
• Maintain and continually build an understanding of how own sub-function integrates with function, alongside knowledge of the organisations products, services and processes within the function.
• Demonstrate understanding of how areas coordinate and contribute to the achievement of the objectives of the organisation sub-function.
• Make evaluative judgements based on the analysis of factual information, paying attention to detail.
• Resolve problems by identifying and selecting solutions through the application of acquired technical experience and will be guided by precedents.
• Guide and persuade team members and communicate complex / sensitive information.
• Act as contact point for stakeholders outside of the immediate function, while building a network of contacts outside team and external to the organisation.

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 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 as a Data Scientist -ML Ops , you should have experience with:

• 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 role is based out of Pune.