Senior Data Scientist

Job Summary

• Strong experience in Python for data science and machine learning
• Hands-on experience with ML frameworks such as Scikit-learn, TensorFlow, or PyTorch
• Experience working with banking or financial services datasets
• Strong knowledge of statistics, predictive modeling, and feature engineering
• Experience working with large datasets and SQL-based databases
• Experience deploying machine learning models in production environments
• Exposure to cloud platforms such as AWS, Azure, or GCP
• Strong data visualization and storytelling capability
  

• Develop machine learning models for banking use cases such as fraud detection, customer segmentation, credit risk modelling, and transaction analytics
• Perform data preprocessing, feature engineering, and exploratory data analysis on large banking datasets
• Build predictive models to support customer growth, risk monitoring, and operational efficiency
• Collaborate with data engineers and BI teams to integrate models into enterprise data platforms
• Deploy and monitor models in production ensuring performance, accuracy, and scalability
• Present insights and recommendations to business and banking stakeholders
• Ensure compliance with banking data governance and regulatory requirements
  

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field is preferred.
• Certifications in Data Science, Machine Learning, or Cloud platforms are a plus
  

• Programming: Python, SQL
• Machine Learning: Scikit-learn, TensorFlow, PyTorch (or similar ML frameworks)
• Data Processing & Analysis: Pandas, NumPy, PySpark/Spark
• Data Visualization: Matplotlib, Seaborn, Plotly, Power BI, Tableau or similar tools
• Data Platforms: Databricks, Snowflake, or modern Data Lake environments
• Cloud Platforms: AWS, Azure, Google Cloud or any cloud enviornments
• Model Deployment & MLOps: Docker, Kubernetes, ML pipelines or similar deployment frameworks.
  

• Strong problem-solving and analytical thinking.
• Excellent communication and collaboration skills.
• Excellent communication and presentation abilities
• Ability to translate complex analytical insights into business impact
• Strong collaboration and teamwork mindset
• Ability to work in fast-paced enterprise environments
• Adaptability to new technologies and tools.
• Creative and innovative mindset.
  

• Experience with data pipeline tools (Apache Airflow, Apache Kafka).
• Experience with Generative AI or LLM frameworks
• Knowledge of MLOps and model lifecycle management
• Knowledge of reinforcement learning and deep learning techniques.
• Exposure to banking analytics areas such as AML, fraud detection, or regulatory reporting.
• Experience with real-time data pipelines or streaming analytics.
• Certifications in Databricks, Dataiku, Snowflake
  

• Minimum of 5-8 years of experience as an Sr Data Scientist. With experience in data engineering and BI.
• Proven track record of developing and deploying Data Science models in production environments.
• Experience collaborating with cross-functional teams including engineering, analytics, and business stakeholders.
  

Compensation & Benefits

• Competitive salary and annual performance-based bonuses
• Comprehensive health and optional Parental insurance.
• Optional Retirement savings plans and tax savings plans.
  

• Development and successful deployment of data science models for banking use cases
• Deliver actionable insights to improve banking products, risk monitoring, and customer engagement
• Ensure high-quality and reliable analytical solutions
• Continuous improvement of model performance and operational efficiency
  

• Model accuracy and predictive performance metrics
• On-time delivery of analytics and machine learning solutions
• Measurable business impact from deployed models
• Production model stability and uptime
• Stakeholder satisfaction and adoption of data-driven solutions