About the Role :
Key Result Areas and Activities :
- Model Development & Analysis : Develop machine learning models for classification, regression, and forecasting tasks using Python and relevant libraries.
- Data Preparation & Feature Engineering : Clean, transform, and prepare structured and unstructured data for modeling and analysis.
- Business Collaboration : Work with business stakeholders to understand requirements and translate them into data science solutions.
- Model Evaluation & Reporting : Evaluate model performance using appropriate metrics and present findings through visualizations and reports.
- Deployment Support : Collaborate with engineering and MLOps teams to support model deployment and monitor performance in production.
Work and Technical Experience
Must Have Skills
Proficiency in Python and data science libraries (e.g., Pandas, NumPy, Scikit-learn).Experience with machine learning techniques and model development.Knowledge of data preprocessing, feature engineering, and model evaluation.Familiarity with SQL and data querying.Exposure to cloud platforms (AWS / GCP / Azure) and version control (Git).Ability to work with large datasets and distributed computing frameworks (e.g., Spark).Strong problem-solving and analytical thinking.Good To Have Skill
Experience with deep learning frameworks (TensorFlow, PyTorch).Knowledge of PySpark or Databricks.Understanding of MLOps practices and CI / CD pipelines.Exposure to GenAI or LLM-based applications.Experience in supply chain, operations, or financial analytics domains.Qualification :
Experience : 3–5 years of experience in data science, machine learning, or analytics roles.Education : Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related field. Master’s degree is a plus.Qualities :
Curiosity and eagerness to learn new technologies.Strong communication and collaboration skills.Attention to detail and commitment to quality.Ability to work independently and in teams.Adaptability in fast-paced environments.