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Truecaller is a global communication safety platform trusted by over 450 million active users across 190+ countries. The company identifies 15+ billion calls daily, helping users avoid spam and scams. With 450+ employees from 45+ nationalities, Truecaller is headquartered in Sweden and operates at significant scale.
As a Senior Data Scientist, you will lead high-impact initiatives at the intersection of data science, product, and engineering. This role combines machine learning, statistics, and data engineering to solve meaningful, real-world problems with strong cross-functional collaboration.
Key responsibilities include:
- Exploring the problem space and identifying data science solutions that drive step-function improvements in customer experience
- Planning and coordinating data science work for the team with emphasis on collaboration
- Gathering, collating, and analyzing data necessary to create data science products
- Collaborating with stakeholders across the organization to build data science products solving business and customer problems at scale
- Coaching and mentoring junior to senior data scientists to improve technical skills and productivity
Required qualifications:
- Bachelor's or Master's degree in Data Science, Machine Learning, Computer Science, Statistics, Mathematics, or related field
- 5+ years of experience using data science and machine learning to solve real-world problems
- Excellent programming skills in Python and SQL
- Solid understanding of Python tooling (IDEs, unit testing frameworks, static code checks)
- Excellent communication and interpersonal skills for presenting complex insights to non-technical stakeholders
- Strong problem-solving skills and results-oriented mindset
- Ability to own and drive projects to production with iterative improvements
Desirable experience:
- Querying large datasets efficiently (BigQuery)
- Collaborating with C-suite executives
- Mobile application data analysis
- Data visualization tools (Tableau, Looker, Power BI)
- Understanding engineering requirements for production ML at scale
