Lead Data Scientist at LexisNexis Risk Solutions transforming complex data into actionable insights. Leading a team to develop reports and dashboards for executive leadership.
MA
Onsite Associate Data Scientist – Gen AI, ML
Posted 2 hours ago
About the role
- Data Scientist developing AI-assisted marketing analytics solutions at Capgemini. Collaborating with teams to deliver innovative solutions and design machine learning models for business growth.
Responsibilities
- Develop and implement AI-assisted marketing analytics solutions
- Work closely with multi-functional teams to deliver innovative solutions
- Design, implement, and optimize machine learning models
- Collaborate with Data Engineers for data acquisition and preprocessing
- Build and maintain data pipelines for model training and deployment
- Ensure data quality and reliability
- Deploy ML models using CI/CD and MLOps practices
- Monitor model performance and manage versioning
- Experiment with new algorithms and technologies
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.)
- Experience in developing and implementing AI-assisted marketing analytics solutions
- Experience with multi-functional teams
- Skills in design, implementation, optimization of machine learning models (supervised, unsupervised, reinforcement learning)
- Experience in projects involving NLP, computer vision, recommendation systems, predictive analytics
- Experience in feature engineering, data preprocessing, and model selection
- Collaboration with Data Engineers to acquire and preprocess large datasets
- Building and maintaining data pipelines to support model training, testing, deployment
- Monitoring model performance, retraining models, managing model versioning
- Staying current with emerging ML/AI technologies, frameworks, research
- Mentoring junior engineers and sharing best practices in ML development and deployment
Benefits
- Paid time off based on employee grade (A-F), defined by policy: Vacation: 12-25 days, depending on grade
- Company paid holidays
- Personal Days
- Sick Leave
- Medical, dental, and vision coverage (or provincial healthcare coordination in Canada)
- Retirement savings plans (e.g., 401(k) in the U.S., RRSP in Canada)
- Life and disability insurance
- Employee assistance programs
- Other benefits as provided by local policy and eligibility
Job title
Job type
Experience level
Salary
Degree requirement
Location requirements
Data Science Manager at Coca - Cola performing complex data research and analysis. Leading a team to create data mining architectures, models and methodologies for business insights.
Data Science Lead managing ML and AI projects at JLL. Leading a team while addressing complex problems and ensuring the products' stability and reliability.
Data Scientist at CVS Health developing algorithms to extract information from legal contracts. Responsible for system improvements and stakeholder communication to drive business results.
Lead AI Strategist at CVS Health guiding data science for supply chain analytics. Driving innovation through strategic AI deployment and optimizing business operations for growth.
GIS Specialist supporting the delivery of geospatial solutions for pricing, underwriting, and risk management decisions. Collaborating with data scientists and engineers in a dynamic environment.
GIS Manager leading the development and implementation of geospatial capabilities at Aviva Canada. Overseeing a team in delivering modern GIS tools and supporting key business areas.
Head of Data and Analytics centralizing insights to build data - driven decision - making framework. Drive digital transformation at Gorilla in the energy sector through innovative data solutions.
Senior Data Scientist developing advanced machine learning models to drive data - driven decision making. Collaborating with stakeholders to identify business solutions using company data.
Data Scientist role at PayPal focusing on data analysis and model implementation for dispute management solutions. Engaging in collaboration and optimization of data processes.