Data Scientist- Pune, Gurgaon Nanostuffs Technologies Pvt. Ltd.
Nanostuffs Technologies Pvt. Ltd.
Office Location
Full Time
Experience: 4 - 4 years required
Pay:
Salary Information not included
Type: Full Time
Location: All India
Skills: Decision Trees, Classification, Regression, Python, SQL, pyspark, supervised machine learning techniques, random forests, Gradient Boosting models
About Nanostuffs Technologies Pvt. Ltd.
Job Description
Position - Data Scientist Location - Gurgaon/Pune Experience - 4+ years We are seeking a Data Scientist with 4+ years of experience, with expertise in PySpark and a strong focus on supervised machine learning techniques, particularly decision trees. The ideal candidate will contribute to building and deploying machine learning models for high-impact business projects, with an emphasis on classification and regression tasks. *Key Responsibilities:** - Focus on supervised learning techniques, with a strong emphasis on decision trees, random forests, and gradient boosting models for solving business problems such as customer segmentation, propensity and risk modeling. - Assist in developing, training, and optimizing supervised machine learning models, particularly decision tree-based methods, to address classification and regression problems. - Support data preprocessing, feature engineering, and transformation tasks using PySpark and structured datasets. - Collaborate with cross-functional teams to translate business requirements into actionable data science solutions. - Stay updated on the latest advancements in supervised machine learning techniques and tools. *Skills & Qualifications:** - Bachelors or Masters in Data Science, Statistics, Computer Science, or a related field. - 4+ years of hands-on experience in data science with a strong focus on supervised learning, particularly decision trees and related algorithms. - Proficiency in Python, PySpark, and SQL for data analysis, feature engineering, and model development. - **Preferred**: Experience in deploying supervised machine learning models into production environments. Job Types: Full-time, Permanent Benefits: Flexible schedule Health insurance Paid sick time Paid time off Provident Fund Schedule: Day shift Monday to Friday Experience: total work: 4 years (Preferred) Work Location: In person,