Principal Data Scientist

About the role

Lead the development and deployment of advanced machine learning solutions that power personalized customer decisioning. Combine hands‑on technical expertise with leadership to deliver scalable models, robust pipelines, and high‑impact insights across the organization.

Key responsibilities

• Design, build and deploy propensity models for cross‑sell and upsell, including Next Best Action/Offer engines.
• Develop production‑grade ML pipelines using Airflow, MLflow or Kubeflow and ensure best‑in‑class MLOps practices.
• Deploy models via APIs or streaming systems (Kafka, Flink) and set up monitoring, drift detection and alerting.
• Collaborate with product managers, engineers and business stakeholders while leading senior data scientists.

Required profile

• 10+ years of experience as a senior data scientist, preferably in banking.
• Bachelor’s degree in Econometrics, Actuarial Science, Mathematics, Statistics, Computer Science or related field.
• Strong analytics background with experience in cloud platforms and GenAI technologies.

Required skills

• Expert‑level Python (mandatory); R or Scala a plus.
• Distributed computing frameworks: Spark, Dask, Ray.
• ML pipeline tools: Airflow, MLflow, Kubeflow.
• Cloud platforms (AWS, GCP, Azure) and data warehouses (Snowflake, BigQuery, Redshift).
• SQL, version control, CI/CD, testing.
• Model deployment tools: SageMaker, Vertex AI, Kafka, Flink.
• Monitoring tools: Prometheus, Grafana, Evidently.
• GenAI: LLMs, embeddings, prompt engineering, vector databases (FAISS, Pinecone).

What we offer

• Competitive, performance‑linked compensation.
• Diverse, inclusive workforce.
• Career development and growth opportunities.
• Opportunity to work with leading experts in the field.