TM Data Science

Job Purpose The role is responsible for delivering specific advanced analytics projects and drive data-driven decision-making and innovation within the organization. This role involves identifying projects, understanding the business need, delivering the development and implementation of advanced analytics, machine learning models, and data strategies to solve complex business problems individually or with team of data scientists. The Senior TM Data Scientist will collaborate with cross-functional teams to ensure the effective use of data, provide strategic insights, and contribute to the overall growth and success of the company. The Senior Data Scientist will operate with a 50% focus on individual contribution and a 50% focus on team management.

• Key Result Areas/Accountabilities:
• Non-Technical Skills

Category Step/Activity

Supporting Actions Analytics Delivery

Use Case / Project Identification

• Supports the VH Digital and Analytics COE in brain storming for use cases.
• Design & Implement prioritization, benchmarking, and feasibility assessment framework aligned with VH Digital and Analytics COE
• Lead translation of business problems into analytics problem statements

Project Execution

• Design & Implement project charter creation (potential timelines, proposed solution architecture, business KPIs, etc.) aligned with VH Digital and Analytics COE
• Lead & Implement
• end-to-end project delivery
• technical standards
• data preparation, feature engineering, and model development
• reproducible pipelines, experiment tracking, and versioning
• evaluation framework, success metrics, and business validation
• Review
• Team member project implementation to ensure code and model quality

Model Monitoring

• Design & Implement
• model performance
• data quality
• drift monitoring
• Lead root-cause analysis and retraining decisions

Solution Adoption

• Design & Lead deployment approach and integration into business processes
• Support user onboarding and adoption tracking

People Management Team Management

• Design & Implement skill roadmaps and development plans (DS and CDS)
• Mentor junior team members (DS, CDS) through reviews and guidance

Training & Knowledge Sharing

• Design & Implement training on DS best practices, tools, and frameworks
• Lead knowledge sharing within DS community

Business Alignment Stakeholder Management

• Lead & Implement stakeholder communication and progress reviews
• Execute translation of analytics outcomes to business impact
• Non-Technical Skills

Category Supporting Actions

Core Skills Concepts (Technical) a) Problem framing, EDA, feature engineering b) Statistical reasoning and ML fundamentals

Concepts (Soft Skills)

• Technical leadership and mentoring
• Business storytelling and stakeholder communication

Tools Python, SQL, R; pandas, numpy; scikit-learn; Jupyter, VS Code; Git

Advanced Skills Concepts a) End-to-end ownership of ML, DL, CV, NLP, GenAI solutions

• Model robustness, monitoring, and optimization

Tools ML: XGBoost, LightGBM DL: TensorFlow, PyTorch CV: OpenCV NLP/GenAI: spaCy, HuggingFace, LangChain, LLM APIsMLOps: MLflow, Airflow, Docker

Optional Skills Concepts a) Advanced experimentation and solution innovation

• Optimization-driven decision making

Tools OR-Tools, PuLP, Prophet, Evidently, Kubernetes, Azure/AWS (any one)