Description

Key Responsibilities: 

•       Team Leadership: Lead the solution and a team of data scientists delivering AI and ML solution for marketing and business engagement use cases 

•       Ownership: Accountability for technical decisions, project outcomes, timelines, and production stability within a defined domain. 

•       Planning and Business alignment: Lead the planning and execution of data science use cases, ensuring alignment with business goals and objectives. 

•       Model Development: Design, train, and optimize machine learning and deep learning models for a variety of marketing and business engagement use cases 

•       Data Analysis: Analyze complex data sets to identify trends, patterns, and actionable insights that can inform business strategies. 

•       Collaboration: Collaborate with stakeholders and cross-functional teams to develop and implement data-driven solutions. 

•       Platform Integration: Enable seamless integration of AI capabilities into business applications and workflows through APIs, SDKs, and microservices. 

•       Stakeholder Communication: Visualize data, create reports, and present findings to senior management and cross-functional teams. 

•       Develop statistical models, analytics, and Machine Learning algorithms using Python and cloud tools (Azure). 

•       Research and Innovation: Stay up to date with the latest advances in AI, Data Science, and Machine Learning. 

•       ML-Ops Best Practices: Optimize platform components for efficiency, scalability, and reliability using best practices in distributed computing, resource management, and cloud-native architectures. 

Essential Business Experience and Technical Skills:

Required: 

•       Bachelor's or master's degree in computer science, Data Science, Engineering, Mathematics, or a related field. 

•       8+ years of overall experience in AI/ML engineering and/or data science. 

•       5+ years of insurance business and/or financial industry experience with sales, marketing, and/or customer engagement analytics. 

•       Proven experience designing, deploying, and operating production ML and/ or GenAI solutions, including APIs, batch, and real-time inference. 

•       Experience in developing Machine Learning models using Python (preferably in the cloud) 

•       Familiarity with best practices for responsible AI, including data privacy, bias mitigation, and/or model monitoring. 

•       Strong SQL knowledge and data analysis skills for data anomaly detection and Exploratory Data Analysis. 

•       Experience with Dominos, Power BI, and/or Azure ML

•       Statistical Knowledge: A strong understanding of statistics and mathematics is essential for data analysis and prediction. 

•       Use predictive modeling or AI solutions to increase and optimize customer experience/communication, revenue generation, ad targeting, and other business outcomes

•       Very good presentation skills to present results clearly and effectively by creating presentations with storytelling, visualizations & results 

•       Very good problem solver and excellent communication skills - both written and verbal 

Preferred: 

•       Experience with employee benefits plans is a plus 

•       Hands-on experience with cloud platforms (Azure/Databricks). 

•       Hands-on expertise with Retrieval-Augmented Generation (RAG) architectures, including integrating external data sources and vector databases to enhance LLM outputs. 

•       Strong understanding of prompt engineering, fine-tuning, and evaluation of generative models for real-world applications. 

Ability to build, optimize, and scale GenAI pipelines for tasks such as document Q&A, summarization, chatbots, and knowledge retrieval

Key Skills

Education

Bachelor's or Master's degrees