PURPOSE:

We are looking for a highly experienced Senior Data Scientist to lead the end-to-end development of AI and Machine Learning solutions that solve complex business challenges and deliver measurable outcomes. The ideal candidate will be responsible for the complete data science lifecycle, including data exploration, statistical modeling, machine learning development, deployment, monitoring, performance optimization, and continuous improvement of production-grade AI/ML systems. This role requires strong expertise in Python, Azure Machine Learning, statistical modeling, forecasting, regression analysis, MLOps, and Large Language Models (LLMs), along with the ability to translate business requirements into scalable, data-driven solutions.

QUALIFICATIONS, SKILLS, AND EXPERIENCE:

• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, Artificial Intelligence, Machine Learning, or a related field.
• Minimum 5+ years of professional experience as a Data Scientist solving real-world business problems using Machine Learning and Artificial Intelligence.
• Strong expertise in Python and object-oriented programming for developing scalable, production-ready AI/ML applications.
• Hands-on experience with Azure Machine Learning, Azure Data Studio, and Azure cloud services for model development, deployment, monitoring, and optimization.
• Strong knowledge of statistical modeling, predictive analytics, regression analysis, forecasting, classification, clustering, probability modeling, and hypothesis testing.
• History of working with Large Language Models (LLMs), Generative AI, NLP, and modern AI frameworks.
• Advanced proficiency in SQL, data extraction, data transformation, data wrangling, and working with large, complex datasets.
• Practical experience deploying, monitoring, and maintaining machine learning models in production environments.
• Knowledge of MLOps practices, model lifecycle management, model monitoring, versioning, automation, and continuous improvement methodologies.
• Strong understanding of machine learning algorithms, deep learning techniques, feature engineering, model evaluation, and performance optimization.
• Experience conducting Root Cause & Corrective Action (RCCA) analysis to identify model performance issues and drive continuous improvements.
• Ability to translate complex analytical findings into actionable business recommendations for technical and non-technical stakeholders.
• Skilled in collaborating within Agile/Scrum environments alongside software engineers, product managers, and business stakeholders.
• Excellent communication, presentation, stakeholder management, and client-facing skills.
• Strong problem-solving mindset with the ability to work independently in fast-paced, evolving environments.

KEY RESPONSIBILITIES:

• AI/ML Solution Development: Design, develop, and implement advanced Machine Learning, Artificial Intelligence, and predictive analytics solutions to solve business and customer challenges.
• Statistical Modeling & Forecasting: Build and optimize statistical models, forecasting models, regression models, classification algorithms, and probability engines that drive business outcomes.
• Data Exploration & Analysis: Collect, clean, transform, analyze, and interpret large and complex datasets to uncover meaningful insights and opportunities.
• Model Development: Develop scalable machine learning models using best practices in feature engineering, model selection, validation, and performance evaluation.
• Azure ML Implementation: Utilize Azure Machine Learning and related Azure services for model training, deployment, monitoring, automation, and lifecycle management.
• Production Deployment: Deploy AI/ML models into production environments and ensure reliability, scalability, security, and operational efficiency.
• Model Monitoring & Optimization: Continuously monitor model performance, identify degradation, and implement improvements to maximize accuracy and business impact.
• Root Cause Analysis (RCCA): Conduct Root Cause & Corrective Action analysis to diagnose model shortcomings and develop strategic improvement roadmaps.
• MLOps & Automation: Implement MLOps best practices including model versioning, monitoring, CI/CD integration, retraining workflows, and automated deployment pipelines.
• LLM & Generative AI Innovation: Evaluate, prototype, and integrate Large Language Models (LLMs) and Generative AI capabilities into business solutions where appropriate.
• Business Problem Translation: Convert ambiguous business challenges into structured, data-driven problem statements and actionable analytics initiatives.
• Code Quality & Engineering Excellence: Write clean, maintainable, modular Python code, participate in code reviews, and uphold engineering best practices.
• Insight Communication: Present analytical findings, recommendations, and model performance results to both technical and non-technical audiences.
• Research & Innovation: Stay current with emerging trends in AI, Machine Learning, Data Science, Generative AI, and cloud technologies to drive innovation.

A Culture of Belonging:
At our core, we value diversity and inclusion. As an equal opportunity employer, we are dedicated to creating a workplace where every voice is heard, every person is respected, and everyone has the opportunity to succeed.