Data Scientist

Swiss Re; Bengaluru; Hybrid; INR 2,500,000 - 5,000,000; Full time

Benefits offered by this job

Hybrid work model

Office presence three days a week

Job summary

Swiss Re is seeking a Data Scientist to join the Non-Life Actuarial Reserving Data & Analytics team in Bengaluru. You will transform data, build dashboards, and develop interactive apps (R Shiny) to support actuarial reserving decisions and communicate results to stakeholders.

The role emphasizes data quality, governance, and collaboration across locations, with a hybrid work model requiring presence in the office at least three days per week.

Qualifications

• 5+ years of relevant work experience.
• University degree in computer science, mathematics, physics, engineering, or another quantitative discipline.
• Proficiency in Python, R, and SQL with demonstrated experience in data transformation and analysis.
• Proficiency in MS Power BI for data visualization and reporting.
• Proficiency in MS Excel for data analysis and reporting tasks.

Responsibilities

• Transform and process data using Python, R, and SQL to support actuarial reserving workflows.
• Design and deliver data visualisations and reports using MS Power BI to communicate insights clearly and effectively.
• Build and maintain interactive applications using R Shiny to support actuarial teams in their day-to-day work.
• Champion data quality and governance by identifying issues, implementing standards, and ensuring data integrity across processes.
• Contribute to transformation projects aimed at modernising future systems and processes.
• Respond to ad-hoc data and reporting requests from reserving actuaries and other stakeholders in a timely and accurate manner.
• Collaborate closely with reserving actuaries and cross-functional teams across multiple locations to deliver impactful analytics solutions.
• Identify and implement process optimisations to continuously improve efficiency and effectiveness of data and analytics workflows.

Skills

Python

R

SQL

Power BI

Excel

R Shiny

Databricks

Palantir Foundry

Apache Spark

Azure

Git

Additional programming langs

Education

University degree in computer science, mathematics, physics, engineering, or another quantitative discipline

Tools

Databricks

Palantir Foundry

Apache Spark

Azure

Job description

Are you someone who gets excited by turning complex data into meaningful insights? Do youthrive at the intersection of technology, analytics, and real-world business impact? If you loveworking with large datasets, building smart solutions, and collaborating with a team ofpassionate specialists — this could be your next great adventure!

About The Role

As a Data Scientist within our Non-Life Actuarial Reserving team, you will be at the heart of data-driven decision-making. Your work will directly support actuarial reserving processes — helpingexperts set reserves, generate insights, and communicate results to key stakeholders. You'llbring your technical expertise to life through data transformation, visualisation, and applicationdevelopment, while also contributing to exciting transformation projects that shape the future ofour systems and processes.This is a role where your curiosity, initiative, and technical skills will truly make a difference.

Key Responsibilities

• Transform and process data using Python, R, and SQL to support actuarial reservingworkflows
• Design and deliver data visualisations and reports using MS Power BI to communicateinsights clearly and effectively
• Build and maintain interactive applications using R Shiny to support actuarial teams in theirday-to-day work
• Champion data quality and governance by identifying issues, implementing standards, andensuring data integrity across processes
• Contribute to transformation projects aimed at modernising future systems and processes
• Respond to ad-hoc data and reporting requests from reserving actuaries and otherstakeholders in a timely and accurate manner
• Collaborate closely with reserving actuaries and cross-functional teams across multiplelocations to deliver impactful analytics solutions
• Identify and implement process optimisations to continuously improve efficiency andeffectiveness of data and analytics workflows

About The Team

You will be part of the Non-Life Actuarial Reserving Data & Analytics team — a dynamic,collaborative group spread across multiple global locations. We are dedicated to deliveringinnovative data and analytics solutions, driving process optimisations, and bringing technologyexpertise to the forefront of actuarial reserving. Our team works hand-in-hand with reservingactuaries to help them set reserves with confidence, produce compelling presentation materials,and share meaningful insights with their stakeholders. We are a team that values curiosity,ownership, and continuous improvement — and we'd love to have you on board!

About You

You are a proactive, detail-oriented professional who takes ownership of your work and is alwayslooking for ways to improve. You manage your time well, stay on top of deadlines, and canprioritise effectively even when juggling multiple tasks. You communicate clearly and confidently— whether you're presenting findings to technical colleagues or explaining complex dataconcepts to non-technical stakeholders. Above all, you are committed to continuous learning andbring a growth mindset to everything you do.

We Are Looking For Candidates Who Meet These Requirements

• At least 5 years of relevant work experience
• University degree in computer science, mathematics, physics, engineering, or anotherquantitative discipline
• Proficiency in Python, R, and SQL with demonstrated experience in data transformation andanalysis
• Proficiency in MS Power BI for data visualisation and reporting
• Proficiency in MS Excel for data analysis and reporting tasks

These Are Additional Nice To Haves

• Experience in re/insurance particularly knowledge of actuarial methods and financialaccounting — a strong plus
• Familiarity with cloud-based analytics platforms such as Databricks or Palantir Foundry
• Experience with Apache Spark for large-scale data processing
• Experience with MS Azure for cloud-based data solutions
• Experience with R Shiny for building interactive data applications
• Experience with Git for version control and collaborative development
• Knowledge of additional programming languages

Our company has a hybrid work model where the expectation is that you will be in the office atleast three days per week.

About Swiss Re

Swiss Re is one of the world’s leading providers of reinsurance, insurance and other forms of insurance-based risk transfer, working to make the world more resilient. We anticipate and manage a wide variety of risks, from natural catastrophes and climate change to cybercrime. We cover both Property & Casualty and Life & Health. Combining experience with creative thinking and cutting-edge expertise, we create new opportunities and solutions for our clients. This is possible thanks to the collaboration of more than 15,000 employees across the world.

Our success depends on our ability to build an inclusive culture encouraging fresh perspectives and innovative thinking. We embrace a workplace where everyone has equal opportunities to thrive and develop professionally regardless of their age, gender, race, ethnicity, gender identity and/or expression, sexual orientation, physical or mental ability, skillset, thought or other characteristics. In our inclusive and flexible environment everyone can bring their authentic selves to work and their passion for sustainability.

If you are an experienced professional returning to the workforce after a career break, we encourage you to apply for open positions that match your skills and experience.

We may use AI-powered tools to support the review and evaluation of applications for this position. These tools provide additional insights to our recruitment teams, but all hiring decisions are carefully reviewed and made by people. To learn more about how we use AI in recruitment and how we handle your personal data, please review our Data Privacy Statement before applying.

Keywords

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