Data Scientist

Job Description Data Scientist

Business Division: IT – Corporate
Department: IT
Location: Hyderabad
Reporting to: Analytics Lead

Position Purpose:

The Data Scientist is responsible for building advanced analytical models and AI/ML solutions that drive actionable insights, automate decision-making, and enable business transformation. The role requires strong problem-solving capabilities, proficiency in statistical and machine learning techniques, and the ability to collaborate with cross-functional teams to embed data-driven decision-making across business functions.

The Data Scientist will manage the entire lifecycle of model development from problem definition data acquisition model development evaluation deployment and monitoring. The role also contributes to the development of reusable assets, AI accelerators, and model governance standards across the organization.

• Qualification: Technical graduate (Engineering degree) or Graduate in Statistics, Applied Mathematics, Data Science, or a related quantitative field. Preferred certifications: Python, Azure ML, TensorFlow, or equivalent

• Experience: 710 years of experience in data science, machine learning, or advanced analytics. Proven track record in delivering business-impacting solutions using predictive modelling, optimization, or AI technologies. Experience in handling large datasets and working across diverse business domains.

Position Org

• Reportees:
• Individual Contributor

Key Responsibilities

• Problem Identification & Scoping
• Work closely with business stakeholders to understand key challenges and opportunities
• Define clear analytical objectives and translate them into data science problems
• Identify feasibility based on data availability and technical constraints
• Data Preparation & Exploration
  • Perform Data acquisition from structured and unstructured sources
  • Conduct exploratory data analysis, feature engineering, and hypothesis testing
  • Collaborate with data engineering teams to ensure reliable data pipelines
• Model Deployment and Monitoring
  • Deploy models into production environments using CI/CD pipelines or APIs
  • Collaborate with DevOps and IT teams for integration into enterprise systems
  • Monitor model performance, decay, and ensure periodic retraining
• Business Impact & Value Realization
  • Translate model outputs into business-friendly insights and decision aids
  • Quantify impact through cost savings, revenue lift, efficiency gains, etc.
  • Present findings to business and leadership teams in a compelling manner
• Collaboration and Mentorship
  • Partner with Business Analysts, Domain SMEs, and Data Engineers on solution development
  • Mentor junior data scientists and analysts in techniques and tools
  • Contribute to AI/ML knowledge base, reusable codes, and best practices
• Governance & Compliance
  • Ensure all models adhere to internal governance frameworks and regulatory norms
  • Document models for reproducibility and auditability
  • Work with IT Security to ensure data privacy and model security

Technology Background

• Programming: Proficiency in languages like Python(preferred) and R, as well as SQL for database interactions.
• Statistics and Mathematics: Strong foundation in statistical methods, probability, and linear algebra
• Machine Learning: Knowledge of various algorithms and their applications in data analysis and prediction, ML/AI Frameworks: Scikit-learn, XGBoost, TensorFlow, Keras, PyTorch
• Data Tools: Pandas, NumPy, Spark, Databricks
• Visualization: Power BI, Tableau, Plotly, Seaborn
• ML Ops: MLflow, Azure ML, AWS SageMaker, Airflow
• Databases: Understanding of database systems like SQL and NoSQL- example MS SQL Server, PostgreSQL, MongoDB, Snowflake
• Big Data Technologies: Familiarity with tools like Hadoop and Spark for handling large datasets
• Cloud: Experience with cloud platforms like AWS, Azure, or Google Cloud for data storage and processing–Azure (preferred), AWS, GCP
• Version Control: Git, Azure DevOps
• Other: Familiarity with NLP, time series forecasting, LLMs, or GenAI models
• Data Security: Understanding of data protection and security measures.

Performance Monitoring

• Outputs
• Accuracy and performance metrics of deployed models (e.g., AUC, RMSE, F1 Score)
• Value generated (e.g., cost reduction, revenue uplift, efficiency improvements)
• Quality and reusability of model code and documentation
• Adoption and usage of analytical solutions by business teams
• Review Methods
• Model performance dashboards and drift analysis
• Post-implementation value tracking reports
• Code reviews and reproducibility audits
• User feedback and stakeholder satisfaction surveys

Engagement

• Internal:
  • Business Functions (Marketing, Sales, Operations, Supply Chain, etc.)
  • Data Engineering and Analytics Platform Teams
  • AI/ML Center of Excellence
  • CIO / Analytics Office
  • Compliance & Risk Teams
• External:
  • AI/ML solution vendors and consultants
  • Open-source and academic collaborators
  • Cloud and platform partners (Azure, AWS, etc.)