Engineering Manager, Data Science

• Define and execute Qore’s Data Science strategy aligned with business goals, partner with Product, Engineering, and Technology, to embed AI into Qore’s platform and APIs and ensure compliance with data privacy, security, and regulatory standards.
  


• Build, lead, and mentor a high-performing team of Data Scientists and ML Engineers.
  


• Design and deploy machine learning models and drive end-to-end ML lifecycle: data exploration, feature engineering, modeling, deployment, and monitoring.
  


• Establish and scale MLOps practices (CI/CD for models, monitoring, retraining pipelines).
  


• Manage and mentor a team of AI/ML Engineers, Data Scientists, and MLOps Engineers and translate complex data insights into clear, actionable business recommendations.
  


• Define architecture for feature stores, model training pipelines, real-time inference, and monitoring and drive productionization of models from research to low-latency APIs handling millions of requests per day.
  


• Establish CI/CD for ML, data quality tests, model observability, and drift detection and ensure explainability, auditability, and compliance with NDPR, CBN, PCI-DSS, and model risk standards.
  


• Design a process for real-time transaction scoring, account takeover detection, AML pattern mining and cashflow-based scoring, BNPL limit engines, portfolio risk forecasting, etc.
  


• Design LLM agents for developer support, log triage, regulatory reporting, document extraction and self-serve feature platform, semantic layer for fintech metrics, synthetic data for testing, etc.
  


• Partner with Product to scope AI features that are shippable, compliant, and ROI-positive and work with Risk/Compliance to get models approved and documented per SR 11-7 standards.
  


• Collaborate with Platform/Infrastructure on GPU inference, Kafka/Fluvio streaming, and cost optimization.
  

Requirements

• BSc in Computer Science, Computer Engineering, Mathematics, Statistics, or other related STEM disciplines.
  


• 7+ years of experience in AI/ML and/or Data Science, with at least 2 years of experience leading teams or projects.
  


• Strong programming skills in Python (e.g., Scikit-learn, TensorFlow, PyTorch), with experience working with large-scale data (SQL, Spark, data pipelines).
  


• Proven experience deploying ML models into production environments with good understanding of cloud platforms (AWS, Azure, or GCP).
  


• Excellent stakeholder management and communication skills and strong understanding of risk, fraud, or financial data use cases.
  


• Proven track record shipping ML systems at scale: real-time fraud, credit, or recommendation systems.
  


• Experience in backend engineering: Python, SQL, distributed systems, REST/gRPC APIs and hands-on with modern ML stack (e.g., PyTorch/TensorFlow, XGBoost/LightGBM, dbt, Airflow, MLflow, Feast/Tecton, etc).
  


• Experience with MLOps - Docker, Kubernetes, model serving with Triton/vLLM/TorchServe, monitoring with Arize/Evidently, etc., and deep understanding of data architecture: warehouses like BigQuery/Snowflake, streaming with Kafka, lakehouse patterns
  


• Strong grasp of ML fundamentals: imbalanced data, calibration, fairness, offline/online metrics with experience in highly regulated environments: Fintech, Banking, Payments, or Insurtech.
  


• Experience with LLMs/RAG for structured data extraction, agent workflows, evals and knowledge of Nigerian/African financial data nuances: BVN, NUBAN, NIBSS, credit bureau data, etc.
  


• Prior work experience on model risk management, regulatory audits and contributions to open-source ML/data tools and projects.
  

Benefits