Senior ML Engineer - Data Science

As a Senior ML Engineer, you'll play a central role in building the data science behind the products, from framing new fraud, risk, and intelligence problems to designing and deploying ML models at scale to helping our enterprise customers and go-to-market teams understand and act on the insights we generate.

Responsibilities:

• Design, build, and continuously improve the ML models that power our risk and intelligence products, and take ownership of new signals as they get scoped.
• Take a loosely defined business or customer problem and break it into a clear data problem, articulating value, impact, and complexity before proposing a solution.
• Build anomaly detection, fraud, and risk-modeling approaches, including network/graph-based methods that keep our signals accurate and resistant to adversarial behavior.
• Own model development, deployment, and monitoring end-to-end, partnering with ML/data engineers on scalability, reliability, cost, and dashboards/alerting.
• Design and run experiments (A/B tests, offline customer POCs) to validate new signals before they roll into production.
• Manage and analyze large, multi-country datasets, ensuring data integrity, consistency, and compliance throughout.
• Partner cross-functionally with Product, Engineering, Legal, and GTM/Sales to scope, prioritise, and ship data products on time, acting as a trusted advisor on what the data can and can't responsibly support.

Requirements:

• 5+ years of experience designing, building, and deploying ML models at scale, ideally including risk/fraud, propensity, or behavioral/network scoring use cases.
• Strong grounding in applied machine learning: classification, anomaly detection, propensity/scoring models, clustering, and time-series/drift monitoring.
• Exposure to graph-based analysis or graph ML (network embeddings, community detection, link prediction) is a plus.
• Hands-on experience taking models from research/experimentation into production, comfortable owning scalability, reliability, and monitoring, not just model accuracy.
• Working knowledge of NLP and LLM-based techniques (prompting, summarization, fine-tuning) useful for customer-facing AI insights and on-device text/SMS signal extraction.
• Proficiency in Python and the ML/data stack: Pandas, NumPy, Scikit-learn, TensorFlow, or PyTorch; comfortable with Hugging Face Transformers where relevant.
• Strong SQL skills and experience with large-scale data processing (BigQuery, Spark/PySpark, Hive/Kafka ecosystem).
• Familiarity with database modelling and data warehousing principles.
• Ability to design, run, and interpret experiments and statistical tests to validate model and business impact.
• Strong communication skills, able to explain model output and trade-offs to both engineering peers and non-technical enterprise stakeholders.
• Comfort operating with a strong privacy/compliance mindset. Truecaller's data products are built on abstracted, privacy-safe signals, and you'll need to reason carefully about what can and can't be derived or stored.

It would be great if you also had:

• Experience with graph databases (e. g., Neo4j) or large-scale graph processing frameworks.
• Experience with ML lifecycle tools (Kubeflow, MLflow) and on-device/edge ML deployment.
• Familiarity with Google Cloud Platform (GCP) and BigQuery.
• Prior experience in fraud/risk scoring, credit/alternate data, or contact center/dialer analytics domains.
• Experience working with data resellers, credit bureaus, or enterprise data partners on integrating and explaining model output.