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, predictive analytics, statistics, business intelligence, artificial intelligence, data modelling, experimentation, forecasting, and modern data-driven problem solving.

The Data Scientist will work closely with data, software, IT, analytics, production, operations, supply chain, finance, sales, and other business teams to collect, clean, analyse, model, and interpret data and develop practical insights, predictive models, analytical solutions, dashboards, automation workflows, and decision-support systems.

The role may involve working with Python, SQL, Pandas, NumPy, scikit-learn, statistics, machine learning algorithms, data visualization, Power BI, Tableau, TensorFlow, PyTorch, cloud platforms, data pipelines, generative AI, and modern data-science technologies depending on project and business requirements.

Qualifications

• B.E. / B.Tech / B.Sc. / BCA / MCA / M.Sc. / M.Tech in Computer Science, Information Technology, Data Science, Artificial Intelligence, Machine Learning, Mathematics, Statistics, Economics, Engineering, 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, business analytics, predictive modelling, artificial intelligence, statistical analysis, 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, Business Analyst, Analytics Engineer, Python Developer, Statistical Analyst, Research Analyst, 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 programming and commonly used data-science libraries such as Pandas, NumPy, scikit-learn, SciPy, or similar tools.
• Strong understanding of data analysis, data cleaning, preprocessing, transformation, validation, feature engineering, exploratory data analysis, and data interpretation.
• Basic to good understanding of statistics, probability, distributions, hypothesis testing, correlation, regression, statistical inference, and analytical reasoning.
• Familiarity with supervised and unsupervised machine-learning techniques including regression, classification, clustering, dimensionality reduction, and model selection.
• Understanding of machine-learning evaluation metrics such as accuracy, precision, recall, F1-score, ROC-AUC, MAE, MSE, RMSE, R-squared, or similar metrics.
• Familiarity with scikit-learn, XGBoost, LightGBM, CatBoost, TensorFlow, PyTorch, Keras, or similar machine-learning frameworks will be beneficial.
• Basic to good knowledge of SQL, MySQL, PostgreSQL, SQL Server, Oracle, BigQuery, or similar databases and query languages.
• Familiarity with joins, aggregations, subqueries, window functions, data extraction, transformation, and relational database concepts will be advantageous.
• Exposure to data visualization using Matplotlib, Plotly, Power BI, Tableau, Looker, Excel, or similar tools will be beneficial.
• Familiarity with time-series analysis, forecasting, anomaly detection, recommendation systems, customer analytics, demand forecasting, or predictive maintenance will be considered an advantage.
• Exposure to deep learning, natural language processing, computer vision, neural networks, or advanced machine-learning techniques will be beneficial but is not mandatory.
• Exposure to generative AI, large language models, prompt engineering, embeddings, vector databases, retrieval-augmented generation, or similar technologies will be considered an advantage.
• Familiarity with Hugging Face, LangChain, LlamaIndex, OpenAI-compatible APIs, or similar AI-development frameworks will be beneficial but is not mandatory.
• Exposure to ETL/ELT, data pipelines, Spark, PySpark, Airflow, Kafka, Databricks, dbt, Hadoop, or similar data-engineering tools will be advantageous.
• Familiarity with AWS, Microsoft Azure, Google Cloud, SageMaker, Azure Machine Learning, Vertex AI, or similar cloud and analytics platforms will be considered an advantage.
• Exposure to Git, GitHub, version control, notebooks, reproducible analysis, code reviews, and collaborative development workflows will be beneficial.
• Familiarity with Docker, Kubernetes, CI/CD, MLflow, DVC, Weights & Biases, model deployment, experiment tracking, or MLOps concepts will be advantageous.
• Exposure to REST APIs, JSON, FastAPI, Flask, Django, or similar tools for integrating analytical or machine-learning solutions will be beneficial.
• Understanding of data quality, model validation, bias, explainability, responsible AI, privacy, security, governance, and ethical data-use concepts will be advantageous.
• Exposure to business analytics, KPI development, dashboards, reporting, experimentation, A/B testing, segmentation, forecasting, or decision-support systems will be considered an advantage.
• Experience or exposure to manufacturing analytics, supply-chain analytics, demand forecasting, quality analytics, production optimization, inventory analytics, sales analytics, or FMCG data will be beneficial but is not mandatory.
• Familiarity with Agile, Scrum, Jira, technical documentation, requirements gathering, experimentation, stakeholder communication, or project-management concepts will be advantageous.
• Strong analytical, mathematical, logical, statistical, and problem-solving abilities.
• Good communication, visualization, documentation, presentation, collaboration, and teamwork skills.
• Ability to translate complex data and analytical findings into clear, practical, and actionable business insights.
• Ability to understand business problems and convert them into measurable analytical, statistical, or machine-learning solutions.
• Willingness to work in an on-site environment.
• Internship, academic project, data-science project, machine-learning project, analytics project, Python project, Kaggle project, GitHub project, research project, hackathon, freelance assignment, 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 statistical methods, machine-learning algorithms, visualization tools, cloud platforms, AI technologies, and modern data-science practices.

Job Location: Morena, Madhya Pradesh

Employment Type: Full-time, On-site

Experience: Freshers & 0–5 Years