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About the Role
Lead the development and deployment of advanced machine learning solutions that power personalized customer decisioning, driving best-in-class modeling, MLOps, and data engineering practices. This role combines hands-on technical expertise with leadership to deliver scalable models, robust pipelines, and high-impact insights.
Responsibilities
- Build and deploy propensity models for cross-sell and upsell.
- Develop and manage Next Best Action / Next Best Offer engines.
- Implement supervised learning, uplift modeling, and causal inference techniques.
- Write production-grade ML pipelines using tools like Airflow, MLflow, or Kubeflow.
- Deploy models to production via APIs or streaming systems (Kafka, Flink).
- Manage model versioning, experiment tracking, and deployment using MLflow, SageMaker, or Vertex AI.
- Set up model monitoring, drift detection, and alerting systems.
- Integrate GenAI into decisioning systems or customer-facing products.
- Lead senior data scientists while remaining hands-on.
- Collaborate with product managers, engineers, and business stakeholders.
Requirements
- 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.
- Expert-level coding in Python.
- 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.
- Experience with cloud platforms (AWS/GCP/Azure) and data warehouses (Snowflake, BigQuery, Redshift).
- Strong SQL skills; ability to optimize queries and manage large datasets.
- 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.
- 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.
- Experience with LLMs, embeddings, prompt engineering, and vector databases (e.g., FAISS, Pinecone).
- Ability to integrate GenAI into decisioning systems or customer-facing products.
- Ability to lead senior data scientists while remaining hands-on.
- Comfortable working with product managers, engineers, and business stakeholders.
- 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.
- Modelling experience including knowledge of Cloud and GenAI.
- Strong Analytics experience in the bank sector is an advantage.
Skills
- Python
- R
- Scala
- Spark
- Dask
- Ray
- Airflow
- MLflow
- Kubeflow
- Version control
- CI/CD
- Testing
- AWS
- GCP
- Azure
- Snowflake
- BigQuery
- Redshift
- SQL
- APIs
- Kafka
- Flink
- SageMaker
- Vertex AI
- Prometheus
- Grafana
- Evidently
- LLMs
- Embeddings
- Prompt engineering
- FAISS
- Pinecone
Experience Level
- 10 years' experience in a senior data scientist position
Education Level
- Bachelor's degree in a relevant field including Econometrics, Actuarial Science, Maths/Statistics, Computer Science etc.
Benefits
- 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
- Get opportunities to bring your whole self to the organization and perform to your best.