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.
Requirements
- 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.
Nice to Have
- 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.
Work Arrangement
On-site — Sandpoint, Idaho
Additional Information
- Kochava is an equal opportunity employer committed to building a team culture that celebrates diversity and inclusion.
- Please be advised that Kochava will never ask candidates to pay any fees or provide sensitive financial information at any point during the recruitment or onboarding process.
- We do not charge fees for applications, interviews, training, equipment, or background checks.
