Kochava provides a unified platform with solutions for advertisers and publishers across the omni-channel advertising ecosystem to link media investments to outcomes.
Kochava is an industry leader in the advertising ecosystem, providing tools and technologies for leading brands, agencies, and premium publishers for measurement and attribution, media mix modeling (MMM), and search ads optimization.
We enable the visibility into and management of trillions of data points, hundreds of millions of users, and billions of dollars in lifetime value (LTV) and paid ad spend. Our suite of solutions are used as a growth stack for leading brands and publishers - empowering them to see and manage their data and unleash the power of their connected audiences.
We are looking for a talented and driven Data Scientist to join our team. In this role, you will be a significant and autonomous contributor, delivering end-to-end machine learning solutions and major analytical initiatives that directly drive business value. You will work across model development, experimental design, and data workflows — solving difficult data science problems with pragmatic, scalable solutions that make a real difference to the business.
Role Location: Sandpoint, Idaho
Job Responsibilities
Design, build, and deploy production-grade machine learning models and analytical solutions that are robust, scalable, and maintainable by other data scientists.
Apply a broad range of statistical methods and ML algorithms, using sound judgment on when — and when not — to use them.
Write highly performant, well-documented, and reproducible code in Python and SQL.
Collaborate with product managers, business stakeholders, and engineering teams to clarify requirements, scope analytical work, and deliver on project milestones.
Make thoughtful trade-offs between model performance and interpretability, complexity and simplicity, and computational cost and accuracy.
Identify and resolve data quality issues and analytical gaps; drive improvements to data science workflows and model reliability.
Actively contribute to model reviews, experimental design discussions, team planning, and post-deployment performance evaluations.
Work to find and address root causes of model performance issues, leaving systems better than you found them.
Contribute to on-call support and take ownership of issues, driving resolutions or ensuring clear handoffs.
Automate manual reporting tasks and contribute to operational excellence across the team.
Mentor junior data scientists and actively participate in the hiring and interview process.
Experience/Skills Required
3+ years of hands-on Data Science experience. A formal degree is not required if you have equivalent knowledge gained from experience.
Strong proficiency in Python for statistical programming and machine learning development.
Expertise writing high-performance SQL queries and working with large-scale datasets.
Solid understanding of a broad range of statistical methods and machine learning algorithms.
Demonstrated ability to independently deliver end-to-end model development — from problem definition through production deployment.
Ability to build solutions that are pragmatic, consider business constraints, and can be maintained and extended by others.
Experience working with data visualization tools and communicating analytical findings clearly to non-technical stakeholders.
Strong sense of ownership — you document your work thoroughly, validate it rigorously, and ensure quality at every step.
Collaborative mindset with the ability to work across teams, balance competing requirements, and influence peers constructively.
Bonus
Experience with MLOps practices, model monitoring, or automated reporting pipelines.
Familiarity with experimental design and A/B testing frameworks.
Track record of improving team workflows, data documentation practices, or analytical infrastructure.
Experience mentoring junior data scientists or contributing to onboarding and training programs.
Experience classifying, storing, and handling data in accordance with data governance policies.
Experience with distributed computing and big data technologies such as Apache Spark.
Experience with data visualization tools such as Tableau.
Experience working with cloud data warehouses such as Amazon Redshift or Google BigQuery.
Experience working with high volume and high velocity data in a distributed environment.
