Senior Data Scientist

BigTapp Analytics; Chennai District; On-site; INR 2,500,000 - 3,800,000; Full time

Benefits offered by this job

Annual performance-based bonuses

Health insurance

Parental insurance

Job summary

BigTapp Analytics in Chennai seeks a Senior Data Scientist with 5+ years of banking/domain experience to design, develop, and deploy advanced ML solutions. You will work on customer analytics, risk analytics, fraud detection, and regulatory reporting across large-scale datasets.

Responsibilities include building predictive models, data preprocessing, feature engineering, and delivering actionable insights to stakeholders, while ensuring governance and compliant data practices.

Qualifications

• 5+ years of experience as Senior Data Scientist in banking/finance.
• Strong Python coding for data science.
• Experience with ML frameworks and production deployment.
• Statistical modeling, feature engineering, and data analysis expertise.

Responsibilities

• Develop ML models for banking use cases (fraud detection, customer segmentation, credit risk).
• Preprocess data, engineer features, and perform EDA on large datasets.
• Build predictive models to support growth, risk monitoring, and operations.
• Collaborate with data engineers and BI teams to integrate models into enterprise platforms.
• Deploy and monitor models in production ensuring performance and scalability.
• Present insights and recommendations to banking stakeholders.
• Ensure compliance with data governance and regulatory requirements.

Skills

Python

Scikit-learn

TensorFlow

PyTorch

SQL

ML deployment

AWS

Azure

GCP

Visualization

Education

Bachelor's or Master's in CS/DS/AI

Tools

Pandas

NumPy

PySpark

Databricks

Snowflake

Docker

Kubernetes

Power BI

Tableau

Job description

Job Description:

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: [e-mail]