Data Scientist

About the Role

Saatvik Agro, the agro-ingredient unit of the Saatvik Group, is seeking a Data Scientist for a full-time, on-site opportunity in Morena, Madhya Pradesh, India. This role is suitable for both fresh graduates and experienced professionals interested in data science, machine learning, statistical analysis, and modern data-driven applications.

Key Responsibilities

• Analyze datasets to identify patterns.
• Build predictive models.
• Generate actionable insights.
• Develop analytical solutions.
• Support data-driven decision-making across various business teams, including software, IT, data, analytics, operations, production, supply chain, finance, and sales.

Requirements & Tech Stack

• Python and commonly used data-science libraries such as Pandas, NumPy, scikit-learn, SciPy.
• Understanding of data cleaning, preprocessing, exploratory data analysis, feature engineering, feature selection, missing-value handling, normalization, scaling, and outlier detection.
• Understanding of statistics, probability, hypothesis testing, distributions, correlation, regression, sampling, confidence intervals, and statistical inference.
• Familiarity with supervised and unsupervised machine learning, including regression, classification, clustering, dimensionality reduction, and anomaly detection.
• Basic to good knowledge of SQL, relational databases, joins, aggregations, subqueries, views, data extraction, and database querying.
• Familiarity with version control using Git and GitHub.
• Understanding of data quality, data governance, data privacy, data security, model explainability, bias, fairness, and responsible AI practices.
• Good analytical, mathematical, statistical, logical, and problem-solving skills.
• Good communication, visualization, documentation, collaboration, and presentation abilities.
• Ability to translate business questions into analytical problems and communicate findings clearly and actionably.
• Willingness to work in an on-site environment.
• Strong willingness to learn new data-science techniques, machine-learning frameworks, cloud platforms, analytical tools, AI technologies, and modern data practices.

Beneficial Exposure (Not Mandatory)

• TensorFlow, PyTorch, Keras, XGBoost, LightGBM, CatBoost.
• Model evaluation metrics such as accuracy, precision, recall, F1-score, ROC-AUC, confusion matrix, RMSE, MAE, R², cross-validation, and model validation.
• MySQL, PostgreSQL, SQL Server, Oracle, MongoDB.
• Data visualization using Matplotlib, Plotly, Power BI, Tableau, Excel, Looker.
• Time-series analysis, demand forecasting, predictive modeling, anomaly detection, recommendation systems, optimization.
• Natural language processing, computer vision, deep learning, advanced AI applications.
• Generative AI, large language models, prompt engineering, embeddings, vector databases, retrieval-augmented generation, Hugging Face, LangChain, LlamaIndex.
• ETL/ELT, data pipelines, Apache Spark, PySpark, Airflow, Kafka, Databricks, dbt, Hadoop.
• AWS, Microsoft Azure, Google Cloud, SageMaker, Azure Machine Learning, Vertex AI, BigQuery, Redshift.
• Collaborative coding, experiment tracking, documentation, and reproducible analytical workflows.
• Docker, Kubernetes, MLflow, DVC, Weights & Biases, CI/CD, model deployment, model monitoring, MLOps.
• Manufacturing analytics, supply-chain analytics, sales analytics, demand forecasting, inventory optimization, pricing analytics, finance analytics, operational analytics.
• ERP, CRM, manufacturing systems, reporting systems, dashboards, enterprise-data environments.
• Agile, Scrum, Jira, SDLC, technical documentation, requirements gathering, experimentation, project collaboration.

Qualifications

• B.E. / B.Tech / B.Sc. / BCA / MCA / M.Sc. / M.Tech in Computer Science, Information Technology, Software Engineering, Data Science, Artificial Intelligence, Machine Learning, Mathematics, Statistics, Economics, or a related discipline.
• Candidates without previous full-time data-science experience can also apply.
• Internship, academic project, data-science project, machine-learning project, Python project, analytics project, Kaggle project, research project, dashboard project, GitHub project, hackathon, freelance assignment, startup project, or open-source contribution will be considered but is not mandatory.