Data Scientist

Role Overview

• Building of GenAI and AI solutions, including but not limited to analytical model development and implementation, prompt engineering, general all-purpose programming (e.g., Python), testing, communication of results, front end and back-end integration, and iterative development with clients.
• Collaborating with business teams to understand their business problems, gather requirements, create initial hypothesis, and development and deployment of GenAI and AI solution approach.
• Designing and solutioning AI/GenAI architectures (LLM/SLM/Machine learning models) to address real business problems.
• Leading and contributing to development of proof of concepts, pilots, and production use cases for business teams while working with cross-functional teams.
• Generate useful insights based on iterative data analysis and make appropriate recommendations to business partners.
• Continuously monitor and improve the performance of AI solutions through data analysis and testing.
• Strong communication to help business partners better understand the use of data, ML models, and AI solutions.
• Stay up-to-date with the latest trends and advancements in cloud-based machine learning platforms, technologies, and tools.

Requirements

• At least 2-year of industry experience in developing and deploying models using AI and GenAI techniques
• Proficient in programming using Python and SQL
• Master’s degree or PhD degree in quantitative fields such as Statistics, Applied Mathematics, Data Science, Engineering, or Computer Science or Physics.
• Experience in using Python (e.g., Pandas, NLTK, Scikit-learn, Keras etc.)
• Experience with common LLM development frameworks (e.g., Langchain, Semantic Kernel)
• Hands-on experience in developing models solving NLP tasks, including Document Classification, Entity Extraction, Entity Relation Extraction, Sentiment Analysis, etc.
• Experiences in fraud prevention and deep-fake detection
• Strong understanding of medical terminology, EHR data, and coding systems.
• Proven problem-solving abilities, including conducting root cause analysis to address specific business inquiries and identify opportunities for enhancement.
• Excellent communication skills to explain complex topics to diverse audiences.
• Demonstrated expertise in the data analytics life cycle, encompassing problem framing, data collection, data cleansing, insights generation, reporting, and communication.
• Skilled in machine learning modeling life cycle, including exploratory data analysis, data cleansing, feature engineering, model building, deployment, and monitoring.
• Good understanding of vectorization and embedding, prompt engineering, RAG, Multi-agent techniques.
• Solid knowledge and highly skilled in supervised and unsupervised machine learning algorithms, deep learning, and LLMs.
• Experience in developing and deploying models in cloud-based environments, specifically Microsoft Azure, and Databricks, following MLOps best practices.
• Experience with Git Version Control, Unit/Integration/End-to-End Testing, CI/CD, release management, etc.

Tech Stack

• Azure
• Cloud
• Keras
• Pandas
• Python
• Scikit-Learn
• SQL

Benefits

• health insurance
• dental
• mental health
• vision
• short
• and long-term disability
• life and AD&D insurance coverage
• adoption/surrogacy and wellness benefits
• employee/family assistance plans
• retirement savings plans (including pension/401(k) savings plans and a global share ownership plan)
• financial education and counseling resources
• paid time off (including up to 11 paid holidays, 3 personal days, 150 hours of vacation, and 40 hours of sick time)