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 early-career professionals interested in working across Data Science, Machine Learning, Python, SQL, Statistics, Predictive Analytics, Forecasting, Data Visualization, and Business Analytics.
The Data Scientist will work closely with data, IT, software, production, operations, supply chain, finance, sales, quality, and other business teams to analyse data, develop predictive models, generate actionable insights, and support data-driven decision-making.
Qualifications
B.E. / B.Tech / B.Sc. / BCA / MCA / M.Sc. / M.Tech in Data Science, Computer Science, Artificial Intelligence, Machine Learning, Statistics, Mathematics, Economics, Information Technology, Engineering, or a related discipline.
Freshers and experienced candidates are strongly encouraged to apply.
Candidates with 0–5 years of experience in data science, analytics, machine learning, business analytics, statistics, data engineering, or related technology roles can apply.
Candidates currently working as Data Scientist, Associate Data Scientist, Junior Data Scientist, Data Analyst, Machine Learning Engineer, Business Analyst, Analytics Engineer, Python Developer, Data Engineer, or AI Engineer are encouraged to apply.
Candidates from IT services, SaaS, product companies, analytics, consulting, fintech, e-commerce, FMCG, manufacturing, logistics, supply chain, telecom, or other industries are welcome.
Basic to good knowledge of Python programming.
Familiarity with libraries such as Pandas, NumPy, Scikit-learn, SciPy, Matplotlib, XGBoost, LightGBM, or similar tools.
Basic to good knowledge of SQL and relational databases.
Understanding of statistics, probability, hypothesis testing, regression, distributions, and analytical concepts.
Understanding of machine learning, model training, validation, evaluation metrics, feature engineering, and model selection.
Familiarity with MySQL, PostgreSQL, SQL Server, Oracle, MongoDB, or similar databases will be beneficial.
Exposure to visualization tools such as Power BI, Tableau, Looker, Excel, or similar platforms will be an advantage.
Exposure to TensorFlow, PyTorch, deep learning, NLP, computer vision, or generative AI will be beneficial but is not mandatory.
Familiarity with forecasting, time-series analysis, optimization, recommendation systems, or anomaly detection will be an advantage.
Exposure to AWS, Azure, Google Cloud, Databricks, Snowflake, BigQuery, Redshift, or similar platforms will be beneficial but is not mandatory.
Familiarity with Git, GitHub, Jupyter, version control, and collaborative analytical workflows.
Exposure to MLflow, Airflow, Docker, APIs, MLOps, or model-deployment tools will be an advantage but is not mandatory.
Good understanding of data cleaning, data quality, feature creation, exploratory analysis, and visualization.
Strong analytical, mathematical, logical, and problem-solving abilities.
Ability to communicate complex analytical findings in a clear and practical manner.
Good communication, documentation, presentation, and teamwork abilities.
Willingness to work in an on-site environment.
Internship, Kaggle competition, data-science project, GitHub project, academic research, hackathon, dashboard project, ML project, or freelance analytical project will be considered but is not mandatory.
Strong willingness to learn new data-science, machine-learning, analytics, and cloud technologies.

Work arrangement
No