About M&G India
We are M&G India, the strategic innovation and digital hub for M&G. Established in 2003, we have offices in Mumbai and Pune.
Our teams work closely with colleagues across the Group worldwide to drive transformation, build digital capability, and support sustainable growth. By leveraging technology, AI, automation, and process excellence, we bring new ways of thinking to improve outcomes for both customers and colleagues.
Grounded in a vibrant culture with strong foundations, we are central to how M&G is transforming as a business.
About M&G
Our purpose is to give everyone real confidence to put their money to work. With a heritage dating back more than 175 years, we have a long history of innovation in savings and investments, combining asset management and insurance expertise to offer a wide range of solutions.
Our two distinct operating segments, Asset Management and Life, work together to provide access to balanced, long-term investment and savings solutions.
Through telling it like it is, owning it now, and moving it forward together with care and integrity; we are creating an exceptional place to work for exceptional talent.
Overall Job Purpose
Individual would be part of the Methodology & Assumption (M&A) Actuarial Policyholder Data Team within the Actuarial Modelling & Valuation Centre of Excellence in Finance.
A new solution for the validation and transformation of policyholder data used in actuarial modelling and assumption setting is being built.
This role will operate, maintain and make BAU updates to the new data pipelines which will validate, transform and enrich data for use in actuarial modelling and assumption setting. It will use its strong data analysis skills to investigate and resolve data issues found pre-emptively or those raised by stakeholders.
The role contributes to the continuous improvement of data processes, ensuring accuracy, consistency, and clarity in outputs while working within defined guidelines. Communication of findings is clear, structured, and tailored to support stakeholders in progressing with confidence.
Individual is accountable and responsible for the Model Points Data Production and Experience Analysis related tasks as assigned by the Team Manager. The person would act as necessary support to the team by carrying the tasks for the process with an acceptable standard of quality and within agreed timelines.
Primary Key Responsibilities
- Proven ability to translate business problems into data science solutions and deliver measurable outcomes
- Hands-on expertise in model development, validation, tuning and interpretability, ensuring robustness and explainability.
- Experience working with large, complex datasets and performing feature engineering and data preparation.
- Proficiency in Python for data science and machine learning applications.
- Working knowledge of SQL and data manipulation for extracting and preparing datasets.
- Working knowledge with PySpark / Databricks or similar distributed data environments.
- Understanding of financial data, actuarial processes or reporting frameworks (preferred but not mandatory).
- Strong communication and stakeholder management skills, with the ability to clearly explain model outputs, assumptions and limitations.
- Experience reviewing work and guiding junior team members
- Design, build, and maintain scalable data pipelines that validate and transform data to meet actuarial modelling requirements.
- Implement checks and controls to ensure actuarial input data is complete, accurate, reconciled and available when required.
- Work with data suppliers to ensure data loads are complete and controlled.
- Monitor and optimise data performance, resolving issues and improving efficiency where needed.
- Maintain runbooks or support documentation
- Maintain process documentation, data dictionaries, and operational procedures
- Ensure all enhancements are appropriately documented and meet business requirements before implementation.
Additional Responsibilities
- Strong experience in predictive modelling and machine learning, including supervised and unsupervised techniques (e.g. regression, classification, clustering, anomaly detection)
- Collaborate with stakeholders to understand data needs and translate them into effective technical solutions.
- Works independently on well-defined deliverables and escalates material risks and issues appropriately
- Work with suppliers and actuarial users to resolve data exceptions.
- Contribute to data governance, including documentation, standards, and controls.
- Deliver high-quality outputs with strong attention to detail, ensuring accuracy, consistency, and compliance with agreed standards
- Work independently within established frameworks, managing own and team’s workload management.
Knowledge, Experience & Educational Qualifications
Knowledge & Skills (Must Have) :
- Exposure to MLOps concepts (model deployment, monitoring, lifecycle management)
- Applied Python, SQL and Databricks (PySpark, Delta, jobs/workflows) skills
- GitHub and DevOps for source control, code review and CI/CD
- Python for data science (e.g. pandas, scikit-learn, PyTorch, statistical modelling)
- Statistical and analytical thinking applied to real-world problem solving.
- Data preparation, feature engineering and handling structured/unstructured datasets.
- Ability to link model outputs to business decisions and insights.
- Clear documentation of models, assumptions and results in an audit-aware environment
- Strong understanding of data structures, databases, and data modelling concepts.
- Experience working on data pipelines with data transformation, validation and storage processes.
- Familiarity with data governance and best practices in data management.
- Proactive approach to identifying and resolving issues.
- Experience maintaining documentation, controls, and governance evidence to support auditability and operational resilience
- People Management
Knowledge & Skills (Additional) :
- Predictive modelling and machine learning (model selection, evaluation, validation, interpretability)
- Ability to interpret financial methodology and reporting results
- Process reengineering, control design and audit-ready documentation
- Coaching and technical review of junior colleagues
- Responsible use of AI design and coding assistants (GitHub Copilot, Copilot 365)
- Experience in financial services / insurance / actuarial analytics environments
Experience:
- 7-10 years of relevant Data Scientist experience (depending on role level)
- Financial services experience, or a closely related field
Educational Qualification:
- Bachelor’s Degree / Master’s Degree
- Any Data Scientist Certifications would be an advantage.
We have a diverse workforce and an inclusive culture at M&G Global Services, regardless of gender, ethnicity, age, sexual orientation, nationality, disability or long term condition, we are looking to attract, promote and retain exceptional people. We also welcome those who take part in military service and those returning from career breaks.
