A Brief Overview

As a member of the Enterprise Data Analytics team, the Lead Data Scientist is responsible for applying advanced analytical, statistical, and machine learning techniques to solve complex business problems. This role partners with cross-functional stakeholders to frame business questions, develop predictive and prescriptive models, and deliver insights that drive data-informed decision-making.

This individual will lead analytical solution design end-to-end, from problem framing and data exploration through modelling, validation, and deployment while ensuring results are interpretable, scalable, and aligned to business outcomes.

What You Will Do

Advanced Analytics Modelling

- Develop, test, and deploy statistical models, machine learning models, and analytical frameworks.
- Apply techniques such as regression, classification, clustering, forecasting, and optimisation.
- 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 Operationalisation

- Validate model performance using appropriate metrics and testing approaches.
- Collaborate with data engineering and BI teams to operationalise 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 organisation.

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 skilled 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

- Work in a clean, pleasant, and comfortable office work setting.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Lead Data Scientist (Punjab)
🏢 Trinet Group
📍 Punjab