Job Title: Senior Data Scientist

Location: Chennai, Hyderabad, & Bangalore

Experience: 5 - 8 Years

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

BigTapp Analytics

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