At Kissterra, we are revolutionizing the insurance industry with the world's first Marketing OS - a comprehensive solution tailored to the unique marketing and distribution needs of insurance organizations.

Our intelligent solutions combine the power of AI, machine learning, and real-time data to reshape marketing strategies and drive profitable growth for top insurers.

Founded in 2015 and headquartered in Israel, Kissterra partners with leading US insurance companies, helping them uncover hidden growth opportunities and optimize their marketing budgets with unparalleled precision and efficiency.

As a Data Scientist, you will sit at the very core of our company. This isn't just a role about maintaining models- it’s about transformation. You will be the architect of a "learning machine," turning raw data into usable metrics and developing the statistical algorithms that power our business performance.

You will collaborate with Data Engineers and Product Managers to translate the "voice of the customer" into digital insights that define the industry standard.

Key Responsibilities:

• Drive Actionable Insights: Analyze diverse data sources to create insights that directly fuel business growth.
• Optimize Performance: Build advanced ML algorithms for bidding—utilizing classification and clustering to maximize campaign performance.
• Innovate with GenAI: Leverage Generative AI techniques to query data for intelligent insights and automate decision-making.
• Own the Lifecycle: Lead projects from initial exploration and Proof of Concept (POC) to scalable production environments.
• Scalability & Performance: Ensure our data pipelines and ML models can handle large-scale, dynamic datasets with high efficiency.
• Metric Ownership: Create and own key performance metrics that drive innovative product solutions.

Requirements:

• Experience: 5+ years in Data Science, Machine Learning, or a related field.
• Technical Mastery: Profound expertise in Python and SQL.
• Domain Expertise: Solid background in time series forecasting, clustering, classification, and predictive modeling.
• Industry Knowledge: Experience in Performance Marketing, AdTech, or Campaign Management.
• Data Engineering Savvy: Deep understanding of data pipelines, feature engineering, and cloud-based ML pipelines.
• Innovation: Familiarity with Deep Learning, Reinforcement Learning, or Generative AI.
• Cloud Fluency: Hands-on experience with AWS, GCP, or Azure for ML deployment.
• Communication: Ability to convey complex technical ideas to non-technical stakeholders clearly.
• Experience with the AWS ecosystem (SageMaker, Glue, Athena, Redshift, EMR)- Advantage
• Proficiency with Spark- Advantage