About the Role
Lead the development and deployment of advanced machine learning solutions that power personalized customer decisioning. This role emphasizes best‑in‑class modeling, MLOps, and data engineering practices. It requires a combination of hands‑on technical expertise and leadership to deliver scalable models, robust pipelines, and high‑impact insights across the organization.
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What You Will Bring
Modeling Expertise:
- Advanced experience in building and deploying propensity models for cross-sell and upsell.
- Deep understanding of Next Best Action / Next Best Offer engines.
- Strong grasp of supervised learning, uplift modeling, and causal inference.
Programming & Engineering:
- Expert-level coding in Python (mandatory); familiarity with R or Scala is a plus.
- Experience with distributed computing (Spark, Dask, Ray).
- Proficient in writing production-grade ML pipelines using tools like Airflow, MLflow, or Kubeflow.
- Strong understanding of software engineering best practices: version control, CI/CD, testing.
Data Infrastructure:
- Experience with cloud platforms (AWS/GCP/Azure) and data warehouses (Snowflake, BigQuery, Redshift).
- Strong SQL skills; ability to optimize queries and manage large datasets.
MLOps & Deployment:
- Experience deploying models to production via APIs or streaming systems (Kafka, Flink).
- Proficient in model versioning, experiment tracking, and deployment using MLflow, SageMaker, or Vertex AI.
Monitoring & Observability:
- Ability to set up model monitoring, drift detection, and alerting systems using Prometheus, Grafana, Evidently, or custom dashboards.
- Experience with logging frameworks and performance profiling for ML services.
GenAI (Preferred):
- Experience with LLMs, embeddings, prompt engineering, and vector databases (e.g., FAISS, Pinecone).
- Ability to integrate GenAI into decisioning systems or customer-facing products.
Leadership & Collaboration:
- Ability to lead senior data scientists while remaining hands-on.
- Comfortable working with product managers, engineers, and business stakeholders.
What We Are Looking For
- Bachelor's degree in a relevant field including Econometrics, Actuarial Science, Maths/Statistics, Computer Science, etc.
- 10 years' experience in a senior data scientist position.
- Modeling experience including knowledge of Cloud and GenAI.
- Strong Analytics experience in the banking sector is an advantage.
What’s In It for You
- Pay for performance culture (Competitive and performance-linked compensation).
- Diverse workforce and inclusive culture.
- Career development and growth opportunities by design.
- Work with the best minds in the field.
- Opportunities to bring your whole self to the organization and perform to your best.
