Senior Data Scientist

Sr Data Scientist

Job Summary

We are seeking a highly skilled Senior Data Scientist with 5+ years of experience to design, develop, and deploy advanced machine learning and data science solutions within the banking and financial services domain. The ideal candidate will work on large-scale banking datasets including customer analytics, risk analytics, transaction intelligence, fraud detection, and regulatory reporting. The role requires strong expertise in statistical modeling, machine learning, and data engineering to drive data-driven decision making for banking products and services.

Mandatory Skills

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

Key Responsibilities

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

Technical Skills

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.

Soft Skills

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.

Good to Have

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

Work Experience

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.

Key Result Areas (KRAs)

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

Key Performance Indicators (KPIs)

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

Contact: hr@bigtapp.ai

Seniority level: Mid-Senior level

Employment type: Full-time

Industries: IT Services and IT Consulting and Information Services