Senior Engineering Manager, Data Science

The role:

As a Senior Engineering Manager – Data Science for Truecaller for Business(TfB), you will lead a high-performing team of Data Scientists and Data Analysts driving intelligence, safety, and operational clarity for our enterprise ecosystem. You will sit at the intersection of technical excellence, risk monitoring, and strategic product execution—architecting intelligence engines, shaping risk-mitigation roadmaps, and mentoring top-tier talent.

What you’ll do:

• Own the overarching Data Science strategy and technical roadmap for Truecaller for Business (TfB). Translate business vision into executable data initiatives, define core KPIs, and drive continuous delivery via agile sprint cycles.
• Lead the hands-on architecture, scaling, and production deployment of ML models to drive core business outcomes.
• Serve as the strategic liaison between Data Science, Product, Platform Engineering, and Leadership to shape product scope, features, and risk policies.
• Direct, mentor, and grow a team of Data Scientists and Analysts—fostering an inclusive, high-trust culture with clear career paths.
• Take full accountability for model accuracy, system uptime, and measurable business impact across all products owned by your team.
• Drive standards for model governance, automated monitoring, unit testing, data privacy compliance, and modern ML/data stack adoption

What you bring in:

• Bachelor’s or Master’s degree in Data Science, Machine Learning, Computer Science, Statistics, Mathematics, or a related quantitative field.
• 12+ years of total experience in Data Science/Machine Learning, with at least 2 years in engineering management capacity.
• Mastery of Python, SQL, and the modern ML ecosystem (scikit-learn, TensorFlow/PyTorch, pandas).
• Deep experience querying very large datasets efficiently (BigQuery, Spark, or similar cloud infrastructure).
• Prior experience working with high-volume data, real-time fraud mitigation, network intelligence, or B2B business analytics is highly desirable.
• Solid understanding of MLOps pipelines, containerization (Docker/Kubernetes), and the engineering overhead required to scale real-time predictive models.
• Outstanding communication skills. You must possess the unique ability to explain complex statistical models to non-technical business partners and distill business needs into rigid technical constraints.