Data Scientist

Job description

**Roles and 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 **Job Requirements :** - 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. **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 Technologie**s: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.)