Associate Data Scientist

Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you’d like, where you’ll be supported and inspired by a collaborative community of colleagues around the world, and where you’ll be able to reimagine what’s possible. Join us and help the world’s leading organizations unlock the value of technology and build a more sustainable, more inclusive world.

About the job you’re considering:

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.

Job Description

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.

Your Role

• 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.

Required Skills

• 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.

 

The base compensation range for this role in the posted location is: $46,000 to $111,000.

Capgemini provides compensation range information in accordance with applicable national, state, provincial, and local pay transparency laws. The base compensation range listed for this position reflects the minimum and maximum target compensation Capgemini, in good faith, believes it may pay for the role at the time of this posting. This range may be subject to change as permitted by law.

The actual compensation offered to any candidate may fall outside of the posted range and will be determined based on multiple factors legally permitted in the applicable jurisdiction.

These may include, but are not limited to: Geographic location, Education and qualifications, Certifications and licenses, Relevant experience and skills, Seniority and performance, Market and business consideration, Internal pay equity.

It is not typical for candidates to be hired at or near the top of the posted compensation range.

In addition to base salary, this role may be eligible for additional compensation such as variable incentives, bonuses, or commissions, depending on the pos