Data Scientist

About the Role:

We’re looking for a Data Scientist with strong Machine Learning and Natural Language Processing (NLP) skills to join our growing analytics team in the banking and financial services sector. You’ll help design and deploy models that improve fraud detection, customer insights, and process automation across the business.

This is a fantastic opportunity for someone with solid technical expertise who’s excited about working on real-world problems in financial services — with the backing of a collaborative and forward-thinking team.

Key Responsibilities:

• Develop and deploy ML and NLP models to solve problems in credit risk, fraud, customer segmentation, and operational efficiency.
• Work with both structured and unstructured banking data, including transaction data, support logs, and documents.
• Design experiments (A/B testing), monitor model performance, and implement continuous improvement pipelines.
• Collaborate with stakeholders across Product, Compliance, Risk, and Engineering to understand business needs and deliver high-impact solutions.
• Apply NLP techniques for tasks like sentiment analysis, named entity recognition, topic modeling, and document classification.
• Ensure models comply with internal governance and external regulatory standards.

About You – Must-Haves:

• 5+ years of hands-on experience in data science roles, with a strong focus on Machine Learning and NLP.
• Minimum 2 years of experience in the banking, fintech, or financial services domain.
• Proficiency in Python, SQL, and common ML/NLP libraries (e.g., Scikit-learn, SpaCy, Transformers, NLTK).
• Comfortable working with real-world production data and deploying models to live systems.
• Strong communication skills — able to present findings clearly to technical and non-technical teams.

Bonus Points (Nice to Have):

• Experience with cloud platforms (AWS, GCP, Azure) or containerization (Docker, Airflow).
• Exposure to regulatory-focused data projects (e.g., AML, KYC, Basel frameworks).
• Familiarity with tools like Databricks, Snowflake, or Power BI/Tableau.
• Understanding of CI/CD workflows for ML models.