Principal Data Scientist

We are looking for a highly experienced Senior Data Scientist with deep expertise in the insurance domain to join our team. In this role, you will design, build, and deploy advanced machine learning and statistical models to solve complex insurance business problems. You will work with large-scale structured and unstructured datasets to generate actionable insights that support strategic decision-making across underwriting, claims, pricing, risk, and fraud functions.

This role requires strong hands-on expertise in Python, R, SQL, machine learning, and statistical modeling, along with the ability to translate complex analytical outcomes into clear business insights for senior stakeholders.

Key Responsibilities

• Collect, integrate, and manage structured and unstructured data from multiple internal and external data sources
• Clean, preprocess, and transform raw datasets into analysis-ready formats
• Apply advanced statistical techniques and machine learning algorithms, including supervised, unsupervised, and reinforcement learning
• Develop predictive and analytical models to support insurance use cases such as risk assessment, fraud detection, underwriting, pricing, and customer segmentation
• Implement techniques including regression, classification, clustering, and time series analysis to uncover trends and patterns
• Build, optimize, and fine-tune machine learning models for insurance applications such as claims prediction and pricing models
• Design and execute model deployment strategies and monitor model performance post-deployment
• Translate analytical findings into clear, actionable insights for business leaders and non-technical stakeholders
• Create data-driven reports, visualizations, and dashboards to enable informed decision-making
• Collaborate closely with business analysts, actuaries, product managers, and IT teams to address business challenges using data-driven solutions
• Provide technical leadership, guidance, and mentorship to junior data scientists and team members

Qualifications

Required Qualifications

• 15+ years of overall experience in data science, with a minimum of 5 years in the insurance services industry
• Strong expertise in Python and R for data analysis, machine learning, and statistical modeling
• Advanced proficiency in SQL, including data manipulation and query optimization
• Hands-on experience with machine learning frameworks and libraries such as scikit-learn, TensorFlow, XGBoost, Keras, and PyTorch
• Experience using data visualization tools including Tableau, Power BI, Matplotlib, Seaborn, and ggplot2
• Strong proficiency in data wrangling, feature engineering, and data preprocessing techniques
• Deep understanding of insurance business processes, including underwriting, claims, pricing, risk modeling, and actuarial analysis
• Proven experience applying data science techniques to insurance-specific challenges such as fraud detection, risk assessment, and customer churn prediction
• Strong analytical and problem-solving skills with a data-driven mindset
• Excellent communication and presentation skills, with the ability to explain complex analytical models to non-technical audiences
• Ability to work effectively in cross-functional and collaborative team environments
• Experience with cloud platforms such as AWS, Google Cloud Platform, or Microsoft Azure is a plus

Preferred Qualifications

• Experience working with big data technologies such as Hadoop, Spark, or Databricks
• Familiarity with actuarial science or advanced insurance-specific statistical methodologies
• Advanced experience in Natural Language Processing (NLP) for analyzing textual data such as claims descriptions and policy documents
• Strong background in predictive analytics and risk management within the insurance domain