Data entry Jobs in Cary, NC

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.



Role Descriptions: Data Science
Essential Skills: Data Science
Desirable Skills:
Keyword:
Skills: Digital : Data Science
Experience Required: 8-10