Data Scientist
Location: Navi Mumbai, Maharashtra, India
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
- 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
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