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Unit Manager - Payments - Data Science
Bajaj Finserv Health
posted 1mon ago
Job Role Insights
Flexible timing
Key skills for the job
Job Description
Job Purpose
To lead Machine Learning Gen AI related use cases in Payments Data initiatives for Bajaj Finance Ltd Duties and Responsibilities
Lead the design, development, and deployment of ML and AI models, with a focus on Generative AI and Neural Networks.
- Work with large datasets stored in formats like Delta and Parquet for efficient processing and analysis.
- Build, optimize, and maintain classifiers, leveraging state-of-the-art ML tools and techniques for feature selection and model improvement.
- Conduct preprocessing on structured and unstructured data, ensuring data integrity and preparing it for machine learning pipelines.
- Translate business requirements into actionable AI/ML solutions, guiding projects from ideation to fully operationalized AI/ML deployments.
- Apply Deep Learning techniques (e.g., regression, classification, time series analysis, and topic modeling) to solve a wide range of business challenges.
- Develop solutions for Computer Vision and other areas of machine/deep learning.
- Provide hands-on expertise with tools such as Python, SQL, and PySpark for data manipulation, analysis, and model building.
- Optimize model deployment with GPU/TPU architecture for high-performance AI/ML workflows.
- Work with Azure Databricks and Azure Data Lake to enhance data collection, storage, and processing procedures.
- Analyze large datasets to uncover patterns, insights, and actionable solutions that align with business objectives.
- Ensure the integrity of data used for analysis, cleansing, and validation.
- Stay up to date with the latest AI/ML trends and contribute to the continuous improvement of data science practices within the organization. Key Decisions / Dimensions
Define User Segments and analyze performance of Campaign
Create relevant user segments while analyzing
Data Solutions to Business data problems
Delivery Timeline related.
Adapting Implementing industry best practices
Data Analysis and Value creation
Create insights from predictive statistical modeling, mathematical knowledge, tools, and techniques to solve complex problems and deliver value
Translate business issues into specific requirements to develop analytic solutions, and identify appropriate data to support the solution
Collaborate with Business teams to address business issues and influence change using strategy, industry, and analytical skills
Deliver large-scale programs that integrate processes with technology to help clients achieve high performance
Create foundational data capabilities by leveraging existing data in addition to constantly learning about new technologies.
Design and implement new predictive solutions based upon business needs and requirements. Major Challenges
Co-ordination between multiple teams (Business, IT, Datalake, OPs) and external vendors to enable task delivery
Providing rightful solutions to Business problems with data driven insights
Enabling controls on data sanity and build data engineering pipeline.
Collaborate with client stakeholders to understand and frame business requests, determine how best to leverage machine learning and advanced analytic methods (Ensemble models, Decision trees and Neural networks; Operations research; Statistical modeling such as multivariate techniques) to support business objectives
Efficiently construct, develop, and implement the models in a variety of modeling tools, achieving highly accurate models
Construct and deliver written reports of the analytic findings in a variety of formats (reports, PPT, including visualization of data and findings), formulating recommendations, and effectively presenting the results to potential non-analytic audience Required Qualifications and Experience
a) Qualifications
Bachelor s degree in computer science, Math, Physics, Engineering, or related quantitative field or Post-graduate from a reputed institute with a minimum 2 years of experience as a Data Scientist in Fintech/Payment/Relevant Industry.
Work Experience
Minimum 2 Maximum of 5 Years of experience working as a Data Scientist/Senior Data Scientist
Experience in implementing machine learning techniques and algorithms, such as, Naive Bayes, SVM, Decision Forests. (Mandatory to have)
Deep learning implementation preferably GPT-4o, Hugging Face Transformer or Mosaic (Mandatory to have)
Experience with common data science toolkits, such as Python (Pandas, NumPy, Sci-kit, SciPy etc.)
Great communication skills (ability to convert data language to business language)
Experience with data visualization tools, such as Power BI, PlotLy, Matplotlib etc.
Proficiency in using query languages such as PySpark, SQL, Spark SQL is mandatory.
Experience with NoSQL databases, such as MongoDB, Cassandra will be a plus (not mandatory)
Good, applied statistics skills, such as distributions, statistical testing, regression, etc.
Good scripting and programming skills (Python/R) with OOPs implementation (mandatory to have).
To lead Machine Learning Gen AI related use cases in Payments Data initiatives for Bajaj Finance Ltd Duties and Responsibilities
Lead the design, development, and deployment of ML and AI models, with a focus on Generative AI and Neural Networks.
- Work with large datasets stored in formats like Delta and Parquet for efficient processing and analysis.
- Build, optimize, and maintain classifiers, leveraging state-of-the-art ML tools and techniques for feature selection and model improvement.
- Conduct preprocessing on structured and unstructured data, ensuring data integrity and preparing it for machine learning pipelines.
- Translate business requirements into actionable AI/ML solutions, guiding projects from ideation to fully operationalized AI/ML deployments.
- Apply Deep Learning techniques (e.g., regression, classification, time series analysis, and topic modeling) to solve a wide range of business challenges.
- Develop solutions for Computer Vision and other areas of machine/deep learning.
- Provide hands-on expertise with tools such as Python, SQL, and PySpark for data manipulation, analysis, and model building.
- Optimize model deployment with GPU/TPU architecture for high-performance AI/ML workflows.
- Work with Azure Databricks and Azure Data Lake to enhance data collection, storage, and processing procedures.
- Analyze large datasets to uncover patterns, insights, and actionable solutions that align with business objectives.
- Ensure the integrity of data used for analysis, cleansing, and validation.
- Stay up to date with the latest AI/ML trends and contribute to the continuous improvement of data science practices within the organization. Key Decisions / Dimensions
Define User Segments and analyze performance of Campaign
Create relevant user segments while analyzing
Data Solutions to Business data problems
Delivery Timeline related.
Adapting Implementing industry best practices
Data Analysis and Value creation
Create insights from predictive statistical modeling, mathematical knowledge, tools, and techniques to solve complex problems and deliver value
Translate business issues into specific requirements to develop analytic solutions, and identify appropriate data to support the solution
Collaborate with Business teams to address business issues and influence change using strategy, industry, and analytical skills
Deliver large-scale programs that integrate processes with technology to help clients achieve high performance
Create foundational data capabilities by leveraging existing data in addition to constantly learning about new technologies.
Design and implement new predictive solutions based upon business needs and requirements. Major Challenges
Co-ordination between multiple teams (Business, IT, Datalake, OPs) and external vendors to enable task delivery
Providing rightful solutions to Business problems with data driven insights
Enabling controls on data sanity and build data engineering pipeline.
Collaborate with client stakeholders to understand and frame business requests, determine how best to leverage machine learning and advanced analytic methods (Ensemble models, Decision trees and Neural networks; Operations research; Statistical modeling such as multivariate techniques) to support business objectives
Efficiently construct, develop, and implement the models in a variety of modeling tools, achieving highly accurate models
Construct and deliver written reports of the analytic findings in a variety of formats (reports, PPT, including visualization of data and findings), formulating recommendations, and effectively presenting the results to potential non-analytic audience Required Qualifications and Experience
a) Qualifications
Bachelor s degree in computer science, Math, Physics, Engineering, or related quantitative field or Post-graduate from a reputed institute with a minimum 2 years of experience as a Data Scientist in Fintech/Payment/Relevant Industry.
Work Experience
Minimum 2 Maximum of 5 Years of experience working as a Data Scientist/Senior Data Scientist
Experience in implementing machine learning techniques and algorithms, such as, Naive Bayes, SVM, Decision Forests. (Mandatory to have)
Deep learning implementation preferably GPT-4o, Hugging Face Transformer or Mosaic (Mandatory to have)
Experience with common data science toolkits, such as Python (Pandas, NumPy, Sci-kit, SciPy etc.)
Great communication skills (ability to convert data language to business language)
Experience with data visualization tools, such as Power BI, PlotLy, Matplotlib etc.
Proficiency in using query languages such as PySpark, SQL, Spark SQL is mandatory.
Experience with NoSQL databases, such as MongoDB, Cassandra will be a plus (not mandatory)
Good, applied statistics skills, such as distributions, statistical testing, regression, etc.
Good scripting and programming skills (Python/R) with OOPs implementation (mandatory to have).
Employment Type: Full Time, Permanent
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Flexible timing
Monday to Friday
No travel
Day Shift
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