About the CompanyCoromandel 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 CoromandelWikipedia. 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 CoromandelWikipediaAbout the RoleThe 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 securityQualificationsTechnical 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.
Job Title
Data Scientist