Lead Data Scientist : 953

Lead Data Scientist

Advanced Analytics & Modelling

Develop, test, and deploy statistical models, machine learning models, and analytical frameworks.

Apply techniques such as regression, classification, clustering, forecasting, and optimization.

Ensure models are explainable, reliable, and aligned with business objectives.

Business Problem Framing

Partner with stakeholders to define analytical problems, hypotheses, and success criteria.

Translate ambiguous business questions into well-structured analytical approaches.

Identify key drivers, risks, and opportunities through data exploration and hypothesis testing.

Generative AI & Unstructured Data

Ability to work with unstructured data and apply NLP and LLM‑based techniques to solve complex business problems.

Hands‑on experience with Generative AI approaches, including prompt engineering, retrieval‑augmented generation (RAG), and evaluation of LLM outputs for accuracy, bias and business relevance.

Strong understanding of how GenAI and LLM solutions are designed and integrated, and ability to partner with engineering teams to deliver scalable, governed AI solutions.

Data Exploration & Feature Engineering

Perform exploratory data analysis to uncover patterns, trends, and anomalies.

Engineer features from structured and unstructured data sources.

Assess data quality, bias, and limitations in analytical outputs.

Model Validation & Operationalization

Validate model performance using appropriate metrics and testing approaches.

Collaborate with data engineering and BI teams to operationalize models and insights.

Monitor model performance over time and recalibrate as needed.

Communication & Storytelling

Communicate findings, insights, and recommendations to technical and non-technical audiences.

Translate complex analytical results into clear, actionable business narratives.

Support decision-making with scenario analysis and impact assessments.

Continuous Improvement & Innovation

Stay current with evolving data science methods, tools, and industry trends.

Identify opportunities to apply advanced analytics and AI to new business problems.

Promote analytical best practices across the organization.

Education Qualifications

Bachelor’s Degree in Data Science, Statistics, Mathematics, Computer Science, or related field

Master’s Degree or PhD preferred

Experience Qualifications

• Typically, 8+ years of experience in data science, advanced analytics, or applied statistics
• Hands-on experience with machine learning and statistical modelling techniques
• Experience working with large, complex datasets in an enterprise environment
• Experience partnering with business teams to drive measurable outcomes

Skills and Abilities

• Strong proficiency in Python, R, or similar analytical programming languages
• Strong foundation in statistics and machine learning
• Ability to frame and solve ambiguous business problems
• Experience balancing model sophistication with interpretability
• Strong critical thinking and problem-solving skills
• Ability to communicate complex concepts clearly and effectively
• Strong collaboration and stakeholder engagement skills
• Commitment to high professional and ethical standards
• Licenses and Certifications (preferred)
• Microsoft Certified: Azure Data Scientist Associate (DP 100) or equivalent 
• AWS Certified Machine Learning-Specialty or equivalent cloud ML certification
• Google Cloud Professional Machine Learning Engineer 
• Certified Analytics Professional (CAP/CAP X) by INFORMS preferred 
• TensorFlow or advanced machine learning certifications a plus

Travel Requirements

Minimal Travel Required

Work Environment

This position is 100% in office.