Director and AI & Data Lead shaping and expanding Guidehouse’s AI and analytics capabilities for financial institutions. Leading cross - functional teams in AI solution delivery and business development.
About the role
- Lead Data Scientist spearheading data science initiatives for media networks. Developing retention models to minimize churn and drive subscription growth through strategic collaboration.
Responsibilities
- Leverage structured and unstructured data from various media and entertainment sources to prepare datasets for advanced analytics and modeling.
- Develop and deliver impactful analytical tools and solutions leveraging statistical modeling, machine learning, and data science to uncover business insights and support strategic decision-making.
- Design and apply advanced predictive and machine learning models; including clustering (K-means, hierarchical), classification (KNN, Naive Bayes, CART), time series forecasting, logistic regression, and econometric models to optimize pricing strategies, assess price elasticity, segment customers, and enhance revenue across channels.
- Leverage generative AI and large language models (LLMs) to develop and implement personalized content and messaging strategies across diverse media channels, enhancing audience engagement and campaign effectiveness.
- Assess and validate statistical models using appropriate performance metrics to ensure precision and accuracy such as accuracy, sensitivity, specificity, ROC, AUC.
- Analyze consumer behavior trends and shifts across various digital touchpoints; perform cross-channel attribution analysis to inform targeted retention strategies.
- Monitor and analyze key engagement metrics to assess the performance of subscriber onboarding programs and their impact on long-term retention.
- Interpret complex analytical insights and translate them into clear, actionable business strategies that improve business outcomes.
- Support scalable deployment of data products by following best practices in CI/CD processes and contribute to agile project management through tools like Jira for sprint planning, tracking, and team coordination.
- Collaborate cross-functionally with technical and non-technical stakeholders to gather requirements, define project scope, and lead data science initiatives, demonstrating strong communication, leadership, and team-building skills.
Requirements
- Master’s or PhD in data Science, statistics, computer science, or related quantitative field.
- 5+ years’ experience in data science roles with demonstrated impact on retention, engagement, or churn reduction.
- Advanced skills in Python/R, SQL, and experience with ML libraries (scikit-learn, XGBoost, TensorFlow/PyTorch).
- Strong background in building predictive churn models, CLV, causal inference and uplift modeling.
- Leverage cloud platforms (e.g., AWS, Azure) and big data tools (e.g., Spark, Hive, Databricks), staying current with evolving technologies and Databricks’ architecture for scalable data science workflows.
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
Senior Data Scientist applying advanced analytics and data science techniques for P&C insurance solutions. Collaborating with stakeholders to improve pricing accuracy and risk management.
Data Scientist AI developing end - to - end AI solutions for Airbus. Partnering with business functions to translate challenges into AI opportunities and support AI capability teams.
Data Scientist role at Unimed Porto Alegre developing analytics and AI solutions. Focus on data modeling and machine learning for better decision - making in healthcare.
Data Science Specialist responsible for developing scalable machine learning solutions and mentoring team members at Comgás, enhancing data - driven decision - making processes.
Data Science Specialist at Comgás leveraging AI for business impact through technology innovation. Transforming complex business challenges into scalable analytical solutions.
Analyst role focusing on data - driven insights and portfolio management in education sector at YDUQS. Collaborating across teams to enhance product lifecycle and performance.
Manager, Data Science in Oncology supporting data engineering and R&D projects at Johnson & Johnson. Leading technical contributions and collaborating with diverse teams in healthcare data.
Join Johnson & Johnson as Associate Director in Data Science. Focus on transforming neuroscience clinical trials through AL/ML and digital health technologies.
Manager in Data Science for Oncology, supporting data workflows and engineering requirements in R&D. Collaborate with technical teams to deliver AI - ready data applications for innovative medicine.