Role : Data Scientist, Assurant, GCC-India.

The Data Scientist is responsible for using analytical techniques to solve complex business problems.

This role will work alongside senior team members to leverage machine learning tools that advance Assurants business.

The Data Scientist will present findings to the business and must be comfortable communicating to all levels of the organization.

This position will be in Bangalore at our India location.

What will be my duties and responsibilities in this job?.

Creating Value Through Data (25%) :

- Produce clear, insightful, understandable work products (visualizations, reports, presentations, etc.) to outline business opportunities and actionable recommendations.

- Transform data into insights through leveraging internal and external tools (e.g., R, SAS, Python, SQL, Hadoop, Excel, Sawtooth, Power BI, etc.) to identify and quantify opportunities.

- Build studies that add substantial value to the decision-making process through proactive data analysis, reporting, and research.

Model Development (50%) :

- Develops machine learning solutions for complex business problems; examine data and develop use cases to identify artificial intelligence/machine learning solutions.

- Provides feedback to product and engineering teams on machine learning solution design and development and communicate the approach and implementation of the machine learning solution.

- Works independently with mentorship from senior team members and/or leadership of the Data Science team on leveraging existing frameworks and models for known use cases around machine learning.

- Devises new algorithmic approaches to solving difficult quantitative problems using large scale enterprise data sources.

Communicating Insights (5%) :

- Presents analysis and resulting recommendations to senior management.

- Leverages data to present compelling business cases to optimize investments and operations.

- Communicates and educates both technical and non-technical employees on analytics and data-driven decision making.

Project Management (10%) :

- Leads projects and assists in development of project plans, conducting analysis/modeling, hypothesis testing, presenting complex information for various audiences in simplified terms, and identifying next steps and future opportunity.

- Mentors junior team members in technical proficiency, code reviews, and business acumen.

- Collaborates with team members to prioritize requests requiring multiple resources for thorough project completion within stated timelines.

Support Data Strategies (5%) :

- Remains abreast of developments in the field(s) of insurance, management, and data sciences by attending self-development programs, interacting with peers, and reviewing pertinent literature.

- Incorporates advancements when practicable and cost effective.

- Participate and drive data modeling and governance best practices.

- Proactively engages internal and external teams to discover areas of analytical needs.

- Advances company-wide understanding and implementation of AI and machine learning as well as matures the teams practices and procedures, leveraging learnings from existing implementations.

- Participates in the talent acquisition process by screening and interviewing candidates at all levels.

Product Leadership (5%) :

- Work cross-functionally with business owners to develop innovative advanced analytics products that will increase customer experience, capitalize growth opportunities, deliver competitive advantage and improve decision making.

- Analyze effectiveness of analytical products and services to constantly improve tools, procedures, and workflows that minimize risk and enhance customer experience.

- Ensures data and model governance is established to comply with internal audit requirements and ensures compliance with data governance and data privacy policies.

- Drive Analytics as a Product (AaaP) and provide thought leadership to Assurants Data Analytics COE, questioning traditional wisdom, current standard practices, and capabilities.

What are the requirements needed for this position? :

- 2-5 years Experience in business analysis, research, statistics, applied mathematics, data management, business intelligence, or related fields.

- 2-5 years Experience with data science tools (e.g., R, Python, SQL, Hadoop, Advanced Excel, Tableau, Power BI, Sawtooth, CART, etc) and relational database software.

- 2+ years Experience in data analysis that includes translating insights into recommendations.

- 2+ years Experience in an analytical role involving data extraction, analysis, statistical or machine learning modeling and communication.

- Masters Degree in Statistics, Economics, Applied Mathematics, Computer Science, or Information Management; or equivalent is required.

What are the Preferred requirements needed for this position :

- Masters Degree preferred in business, mathematics, computer science, or related field.

- Strong written, verbal, and interpersonal communication skills.

- Ability to effectively communicate at all levels in the organization.

- Experience with data storytelling and distilling complex information into understandable ideas.

- Experience working in insurance industry.

- Experience with data science tools (e.g., R, SAS, Python, SQL, Hadoop, Advanced Excel, Tableau, Power BI, Sawtooth, CART, etc.) and relational database software.

- Ability to provide innovative approach to problem solving and demonstrate a track record of such.

- Possesses strong analytical and research skills.

- Ability to work independently on all project types.

- Experience in performing statistical analyses, such as predictive modeling, time series analysis, exploratory data analysis, segmentation and cluster analysis, retention, etc.

- Experience with cloud technologies (e.g. Apache Spark, Azure (Databricks, Azure ML, Cognitive Services)).

If no application deadline is provided, this role may be a pipeline requisition, and we'll continue to collect applications on an ongoing basis, or there is no requirement for this information in the location where the role is posted.

Any posted pay range considers a wide range of compensation factors, including candidate background, experience, and work location, while also allowing for salary growth within the position.