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# Senior Data Scientist

- **Company:** Capgemini
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $105,768.0 - $165,277.0
- **Contract:** Temporary contract
- **Skills:** Database Queries, Github, Python (Programming Language), Machine Learning, Backtesting, Standard Sql, Azure Machine Learning, SQL Databases, Feature Engineering, Pytorch, Prophet, Snowflake, Model Validation, Pandas, Scikit Learn, Software Version Control, Service Stack
- **Published:** July 19, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=77694b0fd8e11c48

## About the Role

* Strong Python-based data science experience (Pandas, Scikit-learn).
 * Expertise in time-series forecasting and predictive modeling.
 * Experience with feature engineering, model validation, and statistical analysis.
 * Strong SQL skills and hands-on experience with Snowflake.
 * Experience using Azure Machine Learning and code versioning tools such as GitHub.

Technology Stack:

 * Python
 * SQL
 * Snowflake
 * Azure Machine Learning
 * GitHub
 * Prophet or other forecasting libraries
 * PyTorch (preferred), * Experience with resource and capacity planning analytics.
 * Experience in utility or regulated industry environments.
 * Knowledge of survival analysis and duration modeling techniques.

## Description

stakeholder management
business analytics
mlops
KPI Development
pytorch
sql database
machine learning
python programming

The Senior Data Scientist will develop predictive analytics solutions that translate operational and business health indicators into measurable metrics. The role will focus on forecasting, anomaly detection, risk identification, and recommendation models to support strategic decision-making., * Analyze complex operational datasets to identify trends, risks, and opportunities.

 * Build forecasting and predictive models using time-series techniques.
 * Develop anomaly detection and recommendation engine capabilities.
 * Perform feature engineering, model validation, and back-testing activities.
 * Collaborate with business stakeholders to define measurable success metrics.
 * Present analytical findings and recommendations to leadership teams.


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