Job Description - Data Scientist (AI CoE)

Position Title : Data Scientist - AI Centre of Excellence (CoE)

Reporting To : Head - AI CoE

Industry : Experience in Telecom & Cloud Services preferred.

Qualifications:

- Bachelors/ Masters in a quantitative discipline (Computer science, Statistics, Econometrics, Mathematics, Engineering and Science)

- 7+ years in analytics overall, including at least 3+ years in Gen AI, AI/ML solution delivery, analytics-led consulting, or change management

- Proven track record in delivering Gen AI, AI/ML solutions at-scale and data driven decision making at-scale

- Deep understanding of AI governance, ML Ops, and responsible AI practices

- Strong leadership and stakeholder management skills

- Excellent communication and change-management capabilities

- Familiarity with telecom BSS/OSS or cloud platforms is a plus

Job Summary

- The incumbent will be a senior member of the AI CoE Team that focuses on Predictive Analytics, Digital Analytics, Data Sciences, and Insights & Experimentation.

- The person will report to the Head - AI CoE. He/she will be an individual contributor and lead enterprise-wide AI initiatives by designing scalable solutions, enabling predictive insights, and fostering AI adoption across business units. He/She will be working on multiple initiatives across multiple departments leveraging traditional and big data. Will closely partner with stakeholders to translate business challenges into AI-driven outcomes and ensure adoption.

Key Responsibilities

- Apply Advanced ML Algorithms and Statistics: Regression, Simulation, Scenario Analysis, Time Series Modelling, Classification - Logistic Regression, Decision Trees, SVM, KNN, Naive Bayes, Clustering, K-Means, Apriopri, Ensemble Models - Random Forest, Boosting, Bagging, Neural Networks

- Deep expertise in Python, R, SQL, TensorFlow, PyTorch, Hugging Face, LangChain, LLMs, RAG, Vector Databases, Fine tuning, evaluation etc.

- Good understanding of Azure and GCP, ETL, data pipelines, Spark

- Assess accuracy of new data sources and data gathering techniques

- Perform Exploratory Data Analysis, detailed analysis of business problems and technical environments in designing the solution

- Apply Supervised, Unsupervised, Reinforcement Learning and Deep Learning algorithms

- Delver Proof of Concepts in 6-8 weeks with high accuracy (90%+) and demonstrate the outcomes quickly

- Document use cases, solutions and recommendations

- Work analytically in a problem-solving environment

- Work in a fast-paced agile development environment

- Coordinate with different functional teams to implement models and monitor outcomes

- Work with stakeholders throughout the organization to identify opportunities for leveraging organization data and apply Predictive Modelling techniques to gain insight across business functions - Operations, Products, Sales, Marketing, HR and Finance teams

- Help program and project managers in the design, planning and governance of implementing Data Science solution

Objectives

- Accelerate AI-driven transformation and innovation

- Maximize ROI from AI investments through strategic alignment and execution

- Promote widespread AI adoption across business units

- Ensure responsible, explainable, and secure AI usage

Key Result Areas (KRAs)

- Business Impact (Revenue, EBIDTA, Cost) improvement through application of AI, Gen AI, AI/ML. At least 3 such applications per quarter

- Time-to-market for AI solutions

- AI adoption rate and cross-functional engagement. Business unit NPS

- Accuracy, reliability, and relevance of deployed models. Achieve 20% reduction in operational cost via AI-driven automation.

- Model deployment cycle time, cost savings achieved

- Compliance with AI governance and ethical standards

Expected Outcomes

- Rapid POCs / POTs with application of AI

- Scalable and reusable AI solutions across the enterprise

- Improved decision-making and operational efficiency

- Strong AI governance and minimized risk exposure

Key Competencies

- Product Thinking, Problem solving & Business Value Orientation

- Technical Curiosity & Applied AI Knowledge

- Agile Delivery & Stakeholder Management

- Cross-functional Collaboration

- Data Storytelling & Influential Communication

- Change Management & Adoption Leadership

- Regulatory Awareness & Ethical AI Practices

- Data Driven Decision Making