Manager-Data Analytics

Company Description

About Sutherland

Artificial Intelligence. Automation. Cloud engineering. Advanced analytics. For business leaders, these are key factors of success. For us, they’re our core expertise.
We work with iconic brands worldwide. We bring them a unique value proposition through market-leading technology and business process excellence.

We’ve created over 200 unique inventions under several patents across AI and other critical technologies. Leveraging our advanced products and platforms, we drive digital transformation, optimize critical business operations, reinvent experiences, and pioneer new solutions, all provided through a seamless “as a service” model.

For each company, we provide new keys for their businesses, the people they work with, and the customers they serve. We tailor proven and rapid formulas, to fit their unique DNA. We bring together human expertise and artificial intelligence to develop digital chemistry. This unlocks new possibilities, transformative outcomes and enduring relationships.

Sutherland
Unlocking digital performance. Delivering measurable results.

Job Description

Job Description

Sutherland is seeking an organized and reliable person to join us as a Manager– Data Science. We are a group of driven and supportive individuals. If you are looking to build a fulfilling career and are confident you have the skills and experience to help us succeed, we want to work with you!

Manager – Responsibilities: 

• Data Analytics and Insights:
  • Conduct analytics to identify patterns and generate actionable insights to support strategic decisions.
  • Process Unstructured data to drive actionable insights.
  • Translate quantitative analyses into comprehensive visuals and reports for non-technical audiences.
• Model Development and Validation:
  • Build, validate, measure, and retrain machine learning models, including supervised and unsupervised algorithms.
  • Apply expertise in Natural Language Processing (NLP) and Generative AI to solve complex business challenges.
• Deployment and Collaboration:
  • Collaborate with AI Engineers to deploy machine learning models and set up inference processes.
  • Ensure models are scalable, maintainable, and aligned with organizational goals.

• What Will you focus on :
  • Risk Assessment and Pricing: Developing predictive models to evaluate risks and set accurate premiums. By analyzing historical data, they identify patterns that inform underwriting decisions.
  • Fraud Detection: Implementing machine learning algorithms to detect fraudulent activities by identifying anomalies in claims data. This proactive approach helps in minimizing losses due to fraud.
  • Customer Segmentation and Personalization: Analyzing customer data to segment the market and tailor insurance products to specific groups, enhancing customer satisfaction and retention.
  • Claims Management Optimization: Utilizing data analytics to streamline the claims process, ensuring timely and accurate settlements. This includes predicting claim volumes and identifying potential bottlenecks.
  • Marketing Strategy Enhancement: Assessing the effectiveness of marketing campaigns and identifying opportunities for customer acquisition and retention

Requirements:

• Experience: 6-7 years of experience in Insurance analytics or a related domain
• Education: bachelors degree in Engineering, Statistics, Mathematics, Computer Science, or a related quantitative field.

• Proficiency in programming languages and data analysis tools such as Python, R, PySpark, and SQL.
• Solid experience in developing predictive modeling techniques (look-a-like models, time series forecasting, regression, clustering)
• Ability to design, implement, and refine business rules for optimizing the Claims and Underwriting value chains is a good to have.
• Familiarity with working in cloud environment (AWS/ AZURE), using distributed compute for large datasets, and version control tools (eg Git)

• Data Proficiency: Expertise in handling large-scale Insurance datasets and applying statistical and machine learning methods to drive actionable insights.
• Data Storytelling & Communication: Demonstrated ability to translate complex data insights into clear, compelling narratives and presentations. Adept at communicating technical findings in a relatable manner to non-technical stakeholders.
• Autonomy & Prioritization: Proven ability to work independently, manage multiple projects/workstreams, and prioritize effectively in a fast-paced, data-driven environment.
• Problem-Solving & Collaboration: Demonstrated ability to troubleshoot complex data issues, optimize system performance, and work effectively within a team environment.

Qualifications

Requirements:

• Experience: 6-7 years of experience in Insurance analytics or a related domain
• Education: bachelors degree in Engineering, Statistics, Mathematics, Computer Science, or a related quantitative field.

• Proficiency in programming languages and data analysis tools such as Python, R, PySpark, and SQL.
• Solid experience in developing predictive modeling techniques (look-a-like models, time series forecasting, regression, clustering)
• Ability to design, implement, and refine business rules for optimizing the Claims and Underwriting value chains is a good to have.
• Familiarity with working in cloud environment (AWS/ AZURE), using distributed compute for large datasets, and version control tools (eg Git)

• Data Proficiency: Expertise in handling large-scale Insurance datasets and applying statistical and machine learning methods to drive actionable insights.
• Data Storytelling & Communication: Demonstrated ability to translate complex data insights into clear, compelling narratives and presentations. Adept at communicating technical findings in a relatable manner to non-technical stakeholders.
• Autonomy & Prioritization: Proven ability to work independently, manage multiple projects/workstreams, and prioritize effectively in a fast-paced, data-driven environment.
• Problem-Solving & Collaboration: Demonstrated ability to troubleshoot complex data issues, optimize system performance, and work effectively within a team environment.

Additional Information

All your information will be kept confidential according to EEO guidelines.