Associate Data Scientist

Capgemini•Seattle, WA

1d•Remote

About The Position

The Data Scientist / ML Engineer will demonstrate excellent knowledge of ML algorithms (e.g., Linear Regression, Logistic Regression, Clustering/Segmentation, Decision Tree, Random Forest, GBM, DNN, Naive Bayes, Support Vector Machine, etc.) to lead efforts, teams, projects, and engage with customers. Develop and implement a set of techniques or analytics applications to transform raw data into meaningful information using data-oriented programming languages and visualization software. Apply data mining, data modeling, natural language processing, and machine learning to extract and analyze information from large structured and unstructured datasets. Visualize, interpret, and report data findings. May create dynamic data reports.

Requirements

Excellent knowledge of ML algorithms (e.g., Linear Regression, Logistic Regression, Clustering/Segmentation, Decision Tree, Random Forest, GBM, DNN, Naive Bayes, Support Vector Machine, etc.)
Data-oriented programming languages
Visualization software
Data mining
Data modeling
Natural language processing
Machine learning
Azure Machine Learning and associated SDKs
Azure Data Factory, Azure Databricks, Azure Synapse Analytics, and Azure Data Lake Storage (ADLS)
Azure Kubernetes Service (AKS), Azure Container Instances, or Azure Functions
Azure DevOps or GitHub Actions
Experience with large‑scale structured and unstructured datasets

Nice To Haves

Experience with reinforcement learning paradigms
Experience with computer vision
Experience with recommendation systems

Responsibilities

Responsible for developing and implementing AI-assisted marketing analytics solutions that address customer needs using data science and machine learning.
Work closely with multi-functional teams to deliver innovative solutions that drive business growth and improve customer engagement.
Design, implement, and optimize machine learning models across supervised, unsupervised, and reinforcement learning paradigms.
Develop solutions involving Natural Language Processing (NLP), computer vision, recommendation systems, and predictive analytics.
Perform feature engineering, data preprocessing, model selection, and hyperparameter tuning.
Experiment with and evaluate novel algorithms and advanced ML techniques to solve complex business problems.
Build, train, and deploy models using Azure Machine Learning and associated SDKs.
Design and maintain scalable data and ML pipelines using Azure Data Factory, Azure Databricks, Azure Synapse Analytics, and Azure Data Lake Storage (ADLS).
Deploy and operationalize models using Azure Kubernetes Service (AKS), Azure Container Instances, or Azure Functions.
Implement CI/CD and MLOps best practices using Azure DevOps or GitHub Actions for model versioning, testing, and deployment.
Monitor model performance, data drift, and model health; retrain models as required to ensure continued accuracy and reliability.
Optimize inference performance, cost, and resource utilization in Azure environments.
Collaborate closely with Data Engineers to ingest, transform, and manage large‑scale structured and unstructured datasets.
Ensure high standards of data quality, consistency, security, and governance in compliance with enterprise and regulatory requirements.
Work with software engineers, data scientists, and product managers to seamlessly integrate ML solutions into business applications and platforms.
Stay current with emerging trends in machine learning, AI, and Azure cloud technologies.
Evaluate new tools, frameworks, and libraries to continuously enhance solution quality and performance.
Mentor junior engineers and data scientists, promoting best practices in ML development, cloud architecture, and MLOps.