Data Scientist

Ready to grow your career with an industry leader? Apply today—we’re excited to welcome you. #UatInsurity

What’s In It For U

• Work Where You Thrive:Find a hybrid work environment that allows you to enjoy great work life balance, stay connected and collaborative

• Generous Time Off: Our leadership believes in taking the time you need when you need it.

• Day One Health Benefits + Employer-Matched Retirement Savings: You might think these are table stakes, but we know these matter to you.

• More Than Just Core Values: Our values are fundamental in how we attract, train, and retain top talent.

• Award-Winning Onboarding Program: We set you up from the get-go to make a significant impact from Day One and follow you through your entire first year.

• Real Career Growth Opportunities: We love to promote from within with over 25% of our open roles filled through internal applicants on average.

• Mentorship Program: We support a champion culture and growth mindset by pairing you with experienced, talented leaders to help you grow personally and professionally.

• Insurity Podcasts: Explore podcasts from our Learning & Development team for deeper insight into Insurity products, company innovation, and the business as a whole.

• Continuous Learning Opportunities: Own your development with your own LinkedIn Learning and Kaplan licenses.

• Employee Referral Bonus: Once you come onboard, give Insurity the biggest compliment you can give by referring someone to work here and earn a referral bonus.

Insurity’s NextData Scientist

We are seeking a Data Scientist to join our Predict team, focused on building and maintaining predictive models that support underwriting, claims, and audit use cases across Workers' Compensation and Commercial Auto. This role will be based in India and will play a key part in scaling our data science capabilities.

What Our Data Scientist Will Do

• Develop predictive models using GLM and machine learning techniques such as GBM, Random Forest, and XGBoost
• Perform feature engineering, selection, and transformation to optimize model performance
• Analyze structured and unstructured datasets to uncover insights and support model development
• Indentify and integrate third-party data sources to augment existing datasets and improve model accuracy
• Collaborate with product and engineering teams to integrate models into production environments
• Use AWS tools such as SageMaker and EC2 to build, train, and deploy models
• Document modeling decisions and communicate findings to technical and non-technical stakeholders
• Support model monitoring and performance tracking over time

Who We’re Looking For

• 1-3 years of experience in data science or predictive modeling
• Strong understanding of GLM and ML algorithms (GBM, XGBoost, Random Forest)
• Experience with Python and relevant libraries (scikit-learn, pandas, NumPy)
• Familiarity with AWS tools, especially SageMaker and EC2
• Experience with feature engineering and model evaluation techniques
• Ability to translate business problems into analytical solutions
• Strong communication skills and ability to work collaboratively in a cross-functional team
• Bachelor's or Master's degree in a quantitative field (Statistics, Mathematics, Computer Science, Data Science)
• Active listening
• Analytical and critical thinking
• Self-starter and quick learner
• Detail-oriented
• Ability to collaborate and work independently
• Written and oral English communication
• Time management including work planning, prioritization, and organization
• Sound judgement
• Ability to handle multiple priorities or tasks
• Flexible and adaptable

Proficient knowledge and experience with:

• Experience in insurance or financial services
• Exposure to model deployment and monitoring in production environments
• Familiarity with version control (Git) and agile development practices
• Working knowledge of R to support understanding and potential migration of legacy models

Thank you for your interest in Insurity! Please understand that due to the volume of applicants we receive, only selected candidates will be contacted.