Data Scientist

Job Description – Data Scientist (AI CoE)

Position Details

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

Reporting To: Head – AI CoE

Location: Any

Industry: Experience in Telecom & Cloud Services preferred.

Qualifications

1. Bachelors/ Masters in a quantitative discipline (Computer science, Statistics, Econometrics, Mathematics, Engineering and Science)
2. 7+ years in analytics overall, including at least 3+ years in Gen AI, AI/ML solution delivery, analytics-led consulting, or change management
3. Proven track record in delivering Gen AI, AI/ML solutions at-scale and data driven decision making at-scale
4. Deep understanding of AI governance, ML Ops, and responsible AI practices
5. Strong leadership and stakeholder management skills
6. Excellent communication and change-management capabilities
7. 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

1. 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 
2. Deep expertise in Python, R, SQL, TensorFlow, PyTorch, Hugging Face, LangChain, LLMs, RAG, Vector Databases, Fine tuning, evaluation etc. 
3. Good understanding of Azure and GCP, ETL, data pipelines, Spark
4. Assess accuracy of new data sources and data gathering techniques 
5. Perform Exploratory Data Analysis, detailed analysis of business problems and technical environments in designing the solution 
6. Apply Supervised, Unsupervised, Reinforcement Learning and Deep Learning algorithms 
7. 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 
8. Work in a fast-paced agile development environment 
9. Coordinate with different functional teams to implement models and monitor outcomes 
10. 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 
11. Help program and project managers in the design, planning and governance of implementing Data Science solution 

Objectives

1. Accelerate AI-driven transformation and innovation
2. Maximize ROI from AI investments through strategic alignment and execution
3. Promote widespread AI adoption across business units
4. Ensure responsible, explainable, and secure AI usage

Key Result Areas (KRAs)

1. Business Impact (Revenue, EBIDTA, Cost) improvement through application of AI, Gen AI, AI/ML.  At least 3 such applications per quarter
2. Time-to-market for AI solutions
3. AI adoption rate and cross-functional engagement. Business unit NPS
4. Accuracy, reliability, and relevance of deployed models. Achieve 20% reduction in operational cost via AI-driven automation.
5. Model deployment cycle time, cost savings achieved 
6. Compliance with AI governance and ethical standards

Expected Outcomes

1. Rapid POCs / POTs with application of AI
2. Scalable and reusable AI solutions across the enterprise
3. Improved decision-making and operational efficiency
4. 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