Data Scientist Fin ReData Scientist Fin Re
Swiss Re
Bangalore, KA, IN
13d ago


The Performance Data & Analytics (PD&A) unit is at the core of the Business Performance & Initiatives (BPI) team and focuses on analysing and providing insights on all aspects of the financial performance of the business unit Reinsurance.

Core objective is the enabling of the performance analytics through sourcing various internal and external data sets, maintaining a tactical data environment, and gradually building out the scope of the analytics performed.

The core activities of the Data Scientist in the PD&A team include :

  • Data Pre-processing, Feature Engineering, Exploratory Data Analysis and Model Building / Evaluation
  • Do ad-hoc analysis early on, precisely interpret the results to fine tune the modelling
  • Analyse large, complex data sets by developing statistical and machine learning models based on business requirements
  • Apply Big data analytics, data mining techniques for the gathering of data, and assess data validity
  • Build and train scalable models using best practices, enabling re-use for future project
  • Undertake requirements gathering on business questions to be addressed and related performance drivers and key metrics
  • Develop, and implement analytics models based on the business questions asked and the potential model's advantages and disadvantages
  • Use knowledge of existing data to propose and provide insights on performance drivers
  • Contributing to ad-hoc projects and supporting other team members as required


    The Performance Data & Analytics unit is at the core of the Business Performance & Initiatives (BPI) team. The BPI team combines focused performance projects, the management of the BU's strategic initiatives performance cycle, and innovative and insightful financial performance analytics.

    The BPI team sits within Reinsurance Financial Planning & Analysis (FP&A) and as such is an integral part of the strategic and financial management of the Business Unit (BU) Reinsurance.


  • Overall 2 to 4 years of working experience in insurance analytics, data science or similar function
  • Experience in Machine Learning and Statistics Supervised and Unsupervised learning-Predictive & Prescriptive Analytics, Decision Trees, Random Forest, Text Mining, Regression, PCA
  • Identifying trends, patterns, and outliers in data
  • Experience in Exploratory Data Analysis, Data Pre-processing and Feature Engineering
  • Statistical programming languages, analytical packages / libraries (R, Python)
  • Statistical tools (R studio, Revolution R, Python notebooks)
  • Visualization using RShiny / Tableau etc.
  • Experience with SQL and relational databases, data warehouse platforms
  • Basic understanding of the insurance risk selection, underwriting practices, and experience in analysing the insurance portfolios is a plus
  • Hands on experience performing data mining tasks based on the cross industry standard process for data mining
  • Excellent command of spoken and written English
  • University degree (or equivalent) in quantitative field (e.g. Mathematics, Statistics, Operations Research, Industrial engineering, Computer Science Engineering, Econometrics or Information Technology)
  • HTML, CSS, Java Script experience is considered a plus
  • Spark (Pyspark, SparkSQL considered a plus)
  • Actuarial education is considered a plus
  • Specific soft skills

  • A self-starter and able to solve problems and develop solutions
  • Team player and should be willing to work collaboratively
  • Inquisitive, proactive and willing to learn new technologies
  • Process and delivery mind-set striving for methodological and operational improvements
  • Intellectual curiosity with eagerness to understand insurance business and its economics
  • Specific technical skills

  • Strong analytical skills (statistics, data exploration / visualization)
  • Knowledge of R, Python, SQL, Spark, HTML, CSS, Java Script
  • Working knowledge of Feature Engineering techniques
  • Working knowledge of machine learning techniques such as clustering, decision trees, logistic regression, linear regression, random forests, etc.
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