WIN - Data - Scientist

Prodapt Solutions

8 - 13 years

7 - 11 Lacs

chennai

Posted:1 day ago| Platform:

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Skills Required

data scientist snowflake python data platforms ai power bi analytics cloud bi tools r tensorflow tableau data science use cases predictive modeling azure ml business insights pytorch feature engineering data governance azure ml

Work Mode

Work from Office

Job Type

Full Time

Job Description

Overview

The Data Scientist designs, develops, and deploys predictive models and advanced analytics solutions to drive business insights and operational improvements. This role collaborates closely with data engineers, architects, and business stakeholders to translate requirements into actionable models and supports integration of AI/ML into BI platforms.

Responsibilities

Model Development & Delivery
Build and iterate models for churn prediction, network performance, and other business-critical use cases.
Conduct feature engineering, model training, validation, and deployment.
Collaboration & Integration
Work with data engineers and architects to ensure data readiness and seamless model integration.
Support dashboarding and reporting initiatives in Tableau and other BI platforms.
Innovation & Enablement
Evaluate and implement natural language querying solutions.
Document model development processes and results for governance and reproducibility.
Continuous Improvement
Monitor model performance, retrain as needed, and apply mitigation actions for drift and decay.
Stay current with emerging ML techniques and tools.

Requirements

Technical Expertise
3+ years in data science roles with hands-on experience in predictive modeling and analytics.
Proficiency in Python, R, and ML frameworks (scikit-learn, TensorFlow, PyTorch ).
Experience with cloud data platforms (Snowflake, Azure ML) and BI tools (Tableau, Power BI).
Collaboration & Communication
Strong ability to translate business needs into technical solutions.
Effective communicator across technical and non-technical teams.
Tools & Technologies
Familiarity with feature engineering, model validation, and deployment best practices.
Understanding of data governance and reproducibility standards.

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