Data Scientist

About the Company Coromandel International Limited is a leading Indian agrichemical company, part of the Murugappa Group and a subsidiary of EID Parry (owner of approximately 56–63%) Wikipedia+2Wikipedia+2 . Founded in the early 1960s (as Coromandel Fertilisers), the company is currently headquartered in Chennai with its registered office in Hyderabad Wikipedia . They are one of India’s largest private-sector producers of phosphatic fertilizers and the world’s largest manufacturer of neem-based bio-pesticides Coromandel Wikipedia . Additionally, they lead the market in organic fertilizers and operate the country’s largest agri-retail chain, with 900+ stores serving over 2 crore farmers Coromandel Wikipedia About the Role 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. 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 Qualifications Technical graduate (Engineering degree) or Graduate in Statistics, Applied Mathematics, Data Science, or a related quantitative field. Required Skills 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. Preferred Skills Experience: 7–10 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.