Lead Data Scientist

Key Responsibilities 

• Develops complex queries and performs extensive programming to access, transform, and prepare largescale data for statistical modeling and AI applications.  

• Leads diagnostic, predictive, and prescriptive analytics initiatives to support strategic and operational decision-making.  

• Identifies data inconsistencies, documents assumptions, and addresses data quality gaps to ensure reliable model outcomes.  

• Designs, trains, evaluates, and deploys machine learning and AI models (e.g., supervised/unsupervised learning, NLP, timeseries, optimization). 

• Apply generative AI techniques (e.g., LLMs, embeddings, prompt engineering, agents) to enable automation, insight generation, and intelligent user experiences. 

• Guides model validation, performance monitoring, explainability, and responsible AI practices.  

• Engages with business and technology stakeholders to understand problems, formulate hypotheses, and translate requirements into analytic and AI solutions.  

• Serves as an AI and analytics expert on cross functional initiatives, contributing to enterprise wide data and AI maturity.  

• Prepares and delivers clear, concise insight presentations and recommendations to technical and nontechnical audiences.  

Qualifications 

• 7+ years of experience in data science, analytics, or related quantitative roles.  

• Strong programming skills (e.g., Python, SQL) for data wrangling, modeling, and production ready solutions.  

• Handson experience with statistical modeling and machine learning techniques.  

• Proven ability to translate complex analytical results into business insights.  

• Experience with generative AI, LLMs, vector databases, or AI orchestration frameworks. 

• Familiarity with cloud based data and AI platforms. 

• Experience working in regulated or largescale enterprise environments. 

What Success Looks Like 

• AI solutions that measurably improve efficiency, insight quality, and decision speed. 

• Scalable, well governed models that are trusted by business partners. 

• Clear storytelling that connects AI outcomes to business value.