Cision has one of the largest media and content databases on Earth. Our data science team works on building intelligent and high-impact features driven by machine learning and NLP.

As a Staff Data Scientist, you will be part of our Machine Learning and Data Science team that build and deploy machine learning models used directly in our products. You will work on complex, high-visibility problems and collaborate closely with engineering, product, and analytics teams to turn prototypes into production-grade solutions.

This role is ideal for someone who operates with strong ownership, delivers high-quality work at pace, and acts as a subject-matter expert on key ML components or services.

Key Responsibilities

Develop and deliver complex machine learning and NLP solutions end-to-end.
Analyze large datasets to uncover insights, design features, and guide modeling decisions.
Build, test, and deploy ML models into production systems in partnership with engineering teams.
Work independently to create tools, frameworks, and reusable components that accelerate model development.
Collaborate cross-functionally with product managers, engineers, and analysts to translate prototypes into production-grade models and services.
Demonstrate sound judgment around risk, workload management, prioritization, and escalation.
Act as a go-to SME for your domain and contribute significantly to team commitments and delivery.

Required Qualifications

8+ years of professional experience in a software or ML engineering environment.
5+ years applying advanced statistical or machine learning techniques to real-world business problems.
Advanced degree in a quantitative field (e.g., Statistics, Mathematics, Economics, Computer Science, Machine Learning) or equivalent practical experience.
Proven track record of delivering ML solutions used in production.
Strong proficiency with Python and ML-focused libraries such as Pandas, NumPy, scikit-learn, TensorFlow, or PyTorch.
Hands-on experience with cloud-based ML workflows in GCP and/or AWS (Kubernetes, Nvidia Triton, Vertex AI, Sagemaker, and similar tools)
Proficiency with database query languages (QL) and experience working with large-scale datasets.
Ability to clearly communicate complex technical concepts to non-technical audiences.
Strong sense of ownership, accountability, and commitment to quality.

Preferred Qualifications      

Experience with modern NLP techniques and pretrained model ecosystems.
Experience building scalable data pipelines or model-serving architectures.
Familiarity with MLOps concepts and practices.
Experience in building production-quality and scalable inf inerence systems with Python, Java, or general-purpose languages.
What Your Day-to-Day Might Look Like

Experimenting with various modeling techniques using GCP Vertex AI or AWS SageMaker.
Analyzing model performance and implementing strategies to improve accuracy, robustness, and reliability.
Designing and engineering new features based on exploratory data analysis.
Identifying, evaluating, and adapting open-source models to solve NLP and ML tasks.
Partnering with engineers to optimize ML models for deployment and monitoring.

Cision has one of the largest media and content databases on Earth. Our data science team works on building intelligent and high-impact features driven by machine learning and NLP.

As a Staff Data Scientist, you will be part of our Machine Learning and Data Science team that build and deploy machine learning models used directly in our products. You will work on complex, high-visibility problems and collaborate closely with engineering, product, and analytics teams to turn prototypes into production-grade solutions.

This role is ideal for someone who operates with strong ownership, delivers high-quality work at pace, and acts as a subject-matter expert on key ML components or services.

Key Responsibilities

Develop and deliver complex machine learning and NLP solutions end-to-end.
Analyze large datasets to uncover insights, design features, and guide modeling decisions.
Build, test, and deploy ML models into production systems in partnership with engineering teams.
Work independently to create tools, frameworks, and reusable components that accelerate model development.
Collaborate cross-functionally with product managers, engineers, and analysts to translate prototypes into production-grade models and services.
Demonstrate sound judgment around risk, workload management, prioritization, and escalation.
Act as a go-to SME for your domain and contribute significantly to team commitments and delivery.

Required Qualifications

8+ years of professional experience in a software or ML engineering environment.
5+ years applying advanced statistical or machine learning techniques to real-world business problems.
Advanced degree in a quantitative field (e.g., Statistics, Mathematics, Economics, Computer Science, Machine Learning) or equivalent practical experience.
Proven track record of delivering ML solutions used in production.
Strong proficiency with Python and ML-focused libraries such as Pandas, NumPy, scikit-learn, TensorFlow, or PyTorch.
Hands-on experience with cloud-based ML workflows in GCP and/or AWS (Kubernetes, Nvidia Triton, Vertex AI, Sagemaker, and similar tools)
Proficiency with database query languages (QL) and experience working with large-scale datasets.
Ability to clearly communicate complex technical concepts to non-technical audiences.
Strong sense of ownership, accountability, and commitment to quality.

Preferred Qualifications      

Experience with modern NLP techniques and pretrained model ecosystems.
Experience building scalable data pipelines or model-serving architectures.
Familiarity with MLOps concepts and practices.
Experience in building production-quality and scalable inf inerence systems with Python, Java, or general-purpose languages.
What Your Day-to-Day Might Look Like

Experimenting with various modeling techniques using GCP Vertex AI or AWS SageMaker.
Analyzing model performance and implementing strategies to improve accuracy, robustness, and reliability.
Designing and engineering new features based on exploratory data analysis.
Identifying, evaluating, and adapting open-source models to solve NLP and ML tasks.
Partnering with engineers to optimize ML models for deployment and monitoring.