Posted on: 27/05/2026
Description :
- Perform exploratory data analysis (EDA), data preprocessing, feature engineering, and model validation to derive actionable business insights
- Design, develop, train, evaluate, and deploy machine learning models for real-world business applications
- Apply and optimize machine learning algorithms including :
a. Ensemble Methods (Random Forest, XGBoost, Gradient Boosting)
b. Logistic Regression
c. Support Vector Machines (SVM)
d. Clustering Techniques (K-Means, DBSCAN)
. Classification and Regression models
- Build scalable predictive and analytical models using structured and unstructured datasets
- Own end-to-end data science project execution including :
a. Problem formulation
b. Data collection and preparation
c. Feature engineering
d. Model development
e. Hyperparameter tuning
f. Model evaluation
g. Deployment and monitoring
- Collaborate with cross-functional teams including Product, Engineering, Business, and Data Engineering teams to translate business requirements into analytical solutions
- Develop automated ML pipelines and scalable workflows for continuous model improvement
- Work with large-scale datasets using SQL, Python, and cloud-based data platforms
- Perform statistical analysis, hypothesis testing, and experimental design to support decision-making
- Optimize model performance, scalability, and accuracy for production environments
- Build dashboards, reports, and data visualizations to communicate insights effectively to technical and non-technical stakeholders
- Stay updated with the latest advancements in Machine Learning, AI, Data Science, and Generative AI technologies
- Ensure data quality, governance, and compliance with organizational standards
Required Skills & Qualifications :
- 5+ years of hands-on experience in Data Science, Machine Learning, or Advanced Analytics
- Strong expertise in Python and SQL for data manipulation, analysis, and model development
- Hands-on experience with ML libraries and frameworks such as :
a. Scikit-learn
b. XGBoost
c. Pandas
d. NumPy
e. TensorFlow/PyTorch (preferred)
- Strong understanding of supervised and unsupervised learning techniques
- Experience in feature engineering, model tuning, and performance optimization
- Strong knowledge of statistical modeling, probability, and data analysis techniques
- Experience working with large datasets and distributed data environments
- Familiarity with Snowpark and Snowflake is preferred but not mandatory
- Experience in deploying machine learning models into production environments
- Strong problem-solving, analytical thinking, and debugging skills
- Ability to communicate complex technical concepts clearly to stakeholders
- Experience with cloud platforms such as AWS, Azure, or GCP is an added advantage
Good to Have :
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