Data Scientist

Company Description
Saatvik Agro is the agro-ingredient unit of the Saatvik Group, specializing in high-quality maize-based ingredients used in food, nutrition, animal feed, and industrial applications. The organization focuses on purity and scientific rigor, converting responsibly sourced maize into functional and reliable ingredient solutions. Its products are designed to meet the evolving needs of modern manufacturers who demand consistency, performance, and safety. Guided by the belief that better ingredients create better outcomes, Saatvik Agro aims to support customers in delivering superior products to their markets.
Role Description
We are looking for a Data Scientist for a full-time, on-site opportunity based in Morena, Madhya Pradesh, India.
The role is suitable for fresh graduates and technology professionals interested in data science, machine learning, statistical analysis, predictive modelling, data analytics, artificial intelligence, forecasting, automation, and modern data-driven business applications.
The Data Scientist will work closely with software, IT, data, analytics, operations, production, supply chain, finance, sales, and other business teams to analyze datasets, identify patterns, build predictive models, generate actionable insights, develop analytical solutions, and support data-driven decision-making across the organization.
The role may involve working with Python, SQL, Pandas, NumPy, scikit-learn, statistical techniques, machine-learning frameworks, visualization tools, cloud platforms, data pipelines, dashboards, generative AI technologies, and other modern data-science tools depending on project and business requirements.
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.
Freshers and experienced candidates are strongly encouraged to apply.
Candidates with 0–5 years of experience in data science, machine learning, data analytics, artificial intelligence, predictive analytics, statistical modelling, business intelligence, or related technology roles can apply.
Candidates currently working as Data Scientist, Junior Data Scientist, Associate Data Scientist, Machine Learning Engineer, ML Engineer, AI Engineer, Data Analyst, Senior Data Analyst, Business Intelligence Analyst, Analytics Engineer, Applied Scientist, Python Data Analyst, Research Analyst, Associate Software Engineer, Graduate Data Scientist, or similar roles are encouraged to apply.
Candidates from IT services, SaaS, product companies, technology, consulting, e-commerce, fintech, telecom, analytics, manufacturing, logistics, FMCG, or other industries are welcome.
Basic to good knowledge of Python and commonly used data-science libraries such as Pandas, NumPy, scikit-learn, SciPy, or similar tools.
Understanding of data cleaning, preprocessing, exploratory data analysis, feature engineering, feature selection, missing-value handling, normalization, scaling, and outlier detection.
Basic to good 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.
Exposure to TensorFlow, PyTorch, Keras, XGBoost, LightGBM, CatBoost, or similar machine-learning and deep-learning frameworks will be beneficial.
Familiarity with model evaluation metrics such as accuracy, precision, recall, F1-score, ROC-AUC, confusion matrix, RMSE, MAE, R², cross-validation, and model validation.
Basic to good knowledge of SQL, relational databases, joins, aggregations, subqueries, views, data extraction, and database querying.
Familiarity with MySQL, PostgreSQL, SQL Server, Oracle, MongoDB, or similar relational and NoSQL databases will be beneficial.
Exposure to data visualization using Matplotlib, Plotly, Power BI, Tableau, Excel, Looker, or similar analytical tools will be advantageous.
Familiarity with time-series analysis, demand forecasting, predictive modelling, anomaly detection, recommendation systems, optimization, or similar analytical use cases will be beneficial.
Exposure to natural language processing, computer vision, deep learning, or advanced AI applications will be considered an advantage but is not mandatory.
Familiarity with generative AI, large language models, prompt engineering, embeddings, vector databases, retrieval-augmented generation, Hugging Face, LangChain, or LlamaIndex will be beneficial but is not mandatory.
Exposure to ETL/ELT, data pipelines, Apache Spark, PySpark, Airflow, Kafka, Databricks, dbt, Hadoop, or similar data-engineering technologies will be advantageous.
Exposure to AWS, Microsoft Azure, Google Cloud, SageMaker, Azure Machine Learning, Vertex AI, BigQuery, Redshift, or similar cloud and data platforms will be considered an advantage.
Familiarity with Git, GitHub, version control, notebooks, collaborative coding, experiment tracking, documentation, and reproducible analytical workflows.
Exposure to Docker, Kubernetes, MLflow, DVC, Weights & Biases, CI/CD, model deployment, model monitoring, or MLOps practices will be beneficial but is not mandatory.
Understanding of data quality, data governance, data privacy, data security, model explainability, bias, fairness, and responsible AI practices will be advantageous.
Exposure to manufacturing analytics, supply-chain analytics, sales analytics, demand forecasting, inventory optimization, pricing analytics, finance analytics, or operational analytics will be considered an additional advantage.
Familiarity with ERP, CRM, manufacturing systems, reporting systems, dashboards, or enterprise-data environments will be beneficial.
Familiarity with Agile, Scrum, Jira, SDLC, technical documentation, requirements gathering, experimentation, or project collaboration will be advantageous but is not mandatory.
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 in a clear, practical, and actionable manner.
Willingness to work in an on-site environment.
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.
Candidates without previous full-time data-science experience can also apply.
Strong willingness to learn new data-science techniques, machine-learning frameworks, cloud platforms, analytical tools, AI technologies, and modern data practices.
Job Location: Morena, Madhya Pradesh
Employment Type: Full-time, On-site
Experience: Freshers & 0–5 Years

Work arrangement
No