Building digital financial solutions for businesses with the goal to get them to a place of financial accountability and success-one app at a time.
Role Summary
We are seeking a Data Science Lead to drive the development and deployment of machine learning models that power Pastel’s compliance and risk intelligence products.
This role requires both technical depth and strategic oversight. You will design systems that detect fraud, flag anomalies, and learn in real time as millions of transactions flow through our platform.
You will lead model experimentation, optimization, and deployment while working closely with our engineering, data, and product teams to turn AI research into production-ready impact.
Key Responsibilities
Model Development: Design, build, and train advanced machine learning models for fraud detection, risk scoring, anomaly detection, and customer behavior prediction.
Predictive Analytics: Develop time-series forecasting, recommendation systems, and natural language processing pipelines where relevant.
Model Deployment: Work with engineers to deploy and maintain models in production environments, ideally with experience in real-time inference systems.
Data Architecture: Oversee data pipeline integrity, ensuring availability, scalability, and efficiency for model training and evaluation.
Evaluation and Optimization: Define metrics, monitor performance, and continuously improve models for accuracy, precision, and interpretability.
Collaboration: Partner with product, data engineering, and software teams to align ML initiatives with business and customer goals.
Leadership: Mentor junior data scientists, promote best practices in experimentation, version control, and model governance.
Research: Stay updated on state-of-the-art machine learning and AI trends to inform ongoing innovation.
Required Qualifications
Education: Bachelor’s or Master’s degree in Computer Science, Statistics, Engineering, or a related field.
Experience: At least 5 years of experience as a Data Scientist, Data Engineer, or AI/ML Engineer.
Proven experience in predictive modeling, time-series forecasting, recommendation systems, NLP, or computer vision.
Hands-on experience deploying ML models to production environments.
Familiarity with real-time inference and distributed data systems such as Kafka or Spark.
Strong programming skills in Python (NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow).
Experience with cloud infrastructure (AWS, GCP) and MLOps tools such as MLflow, Docker, or Kubernetes.
Technical Skills
Machine learning frameworks: PyTorch, TensorFlow, Scikit-learn
Data manipulation and pipeline tools: Pandas, Spark, Airflow, SQL
Cloud and deployment: GCP, AWS, Docker, Kubernetes
Experience with APIs and model serving for real-time inference
Version control and experimentation tracking (Git, MLflow, DVC)
Understanding of feature engineering, bias mitigation, and model explainability