Data Scientist - Peer Content Analytics

About The Job

The Data Scientist - Peer Content Analytics is a hands-on technical expert responsible for designing, building, and deploying machine learning, NLP, and AI solutions. You'll work at the crossroads of data science, content strategy, and product innovation, turning large volumes of structured and unstructured data into actionable intelligence that drives strategic decisions.

What You'll Do

• Advanced Analytics & AI Solutions
• Develop, deploy, and maintain scalable machine learning, NLP, and generative AI models using data from Gartner Peer Insights and Peer Community platforms.
• Build reusable pipelines for sentiment analysis, topic modeling, LLM-based summarization, clustering, and other AI-driven content solutions.
• Explore and analyze complex datasets to uncover trends, patterns, and actionable signals that inform research and content strategy.
• Methodology & Experimentation
• Design, implement, and refine scoring systems, ranking algorithms, and statistical models for peer-driven content and publications.
• Establish robust experimentation frameworks, define success metrics, and ensure findings are statistically sound.
• Apply advanced statistical techniques for bias detection, hypothesis testing, regression, and A/B testing.
• Cross-Functional Collaboration
• Partner with business stakeholders to identify opportunities for differentiated peer-driven insights and translate model outputs into actionable narratives for published insights.
• Collaborate with DPT and Engineering to design data pipelines and infrastructure that support model use cases and cross-tool integrations.
• Present complex analytical concepts and model outcomes to non-technical stakeholders in a clear and engaging manner.
• Visualization & Reporting
• Leverage PowerBI to create interactive dashboards and self-service reports that make complex data accessible to a broad audience.
• Ensure data products are well-documented, version-controlled, and tested for reliability and reproducibility.
  
  

Requirements

Requirements

• Bachelor's degree required; Master's degree in a quantitative field (Math, Computer Science, Engineering, Economics, etc.) strongly preferred.
• 2-4 years of hands-on experience in data science or applied ML (predictive modelling), with a proven track record of delivering end-to-end solutions in a business environment.
• Demonstrated ability to translate business objectives into actionable data science projects and quantitative analysis into strategic business recommendations.
• Advanced proficiency in Python (pandas, NumPy, scikit-learn, PyTorch, TensorFlow) and SQL for data extraction, transformation, and validation.
• Deep experience in NLP, including topic modeling, sentiment analysis text classification, and semantic similarity.
• Hands-on experience with LLMs and generative AI frameworks, including cloud deployment on Azure or AWS.
• Experience with MLOps best practices, experiment tracking (MLflow, Weights & Biases), and production monitoring.
• Advanced PowerBI skills for data visualization and interactive reporting.
• Excellent communication and collaboration skills, with a proven ability to present complex modeling decisions and data insights to non-technical audiences.
• Experience in methodology design for content products, including creating consistent rules for ranking, scoring, or evaluating products and companies.
• Proven ability to proactively identify data-driven content opportunities and rapidly convert insights into impactful content.
  
  

Preferred Qualifications

• Experience in building, deploying, or enhancing chatbot solutions using NLP and LLM technologies.
  
  

Skills

PythonSQLMachine LearningNatural Language ProcessingGenerative AIPowerBIPyTorchTensorFlowMLOps

Benefits

• Remote / hybrid work