Qode
About the Role
We are seeking a highly skilled
Data Scientist
with expertise in
Python, R, and PySpark
to join our analytics team for a leading banking client. The ideal candidate will leverage advanced analytics, machine learning, and big data technologies to design data-driven solutions, generate actionable insights, and support strategic decision-making in areas such as risk management, fraud detection, customer segmentation, and credit scoring. Key Responsibilities
Work with large-scale structured and unstructured datasets to design, develop, and deploy predictive and prescriptive models.
Apply statistical modeling, machine learning, and data mining techniques to address complex business problems in banking.
Develop and optimize
data pipelines
and
ETL processes
using
PySpark
for efficient big data processing.
Perform
exploratory data analysis (EDA) , feature engineering, and hypothesis testing using
Python and R .
Partner with business stakeholders to translate requirements into analytical solutions.
Create visualizations and dashboards to communicate insights to technical and non-technical audiences.
Ensure compliance with data governance, privacy, and security standards in all analytics work. Required Skills & Qualifications
Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, or related field.
3-7 years
of experience as a Data Scientist, ideally within the
banking or financial services domain .
Strong programming skills in
Python and R , including libraries such as Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch, ggplot2, and caret.
Hands-on experience with
PySpark
for distributed data processing and analysis.
Strong statistical and mathematical foundation in regression, classification, clustering, time-series analysis, and hypothesis testing.
Experience working with large datasets, big data platforms (Hadoop, Spark), and SQL/NoSQL databases.
Familiarity with
cloud platforms
(AWS, Azure, or GCP) and version control (Git).
Excellent problem-solving, communication, and stakeholder management skills.
Good to have Experience in
risk analytics, fraud detection, credit risk modeling, or customer analytics
in banking/financial services.
We are seeking a highly skilled
Data Scientist
with expertise in
Python, R, and PySpark
to join our analytics team for a leading banking client. The ideal candidate will leverage advanced analytics, machine learning, and big data technologies to design data-driven solutions, generate actionable insights, and support strategic decision-making in areas such as risk management, fraud detection, customer segmentation, and credit scoring. Key Responsibilities
Work with large-scale structured and unstructured datasets to design, develop, and deploy predictive and prescriptive models.
Apply statistical modeling, machine learning, and data mining techniques to address complex business problems in banking.
Develop and optimize
data pipelines
and
ETL processes
using
PySpark
for efficient big data processing.
Perform
exploratory data analysis (EDA) , feature engineering, and hypothesis testing using
Python and R .
Partner with business stakeholders to translate requirements into analytical solutions.
Create visualizations and dashboards to communicate insights to technical and non-technical audiences.
Ensure compliance with data governance, privacy, and security standards in all analytics work. Required Skills & Qualifications
Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, or related field.
3-7 years
of experience as a Data Scientist, ideally within the
banking or financial services domain .
Strong programming skills in
Python and R , including libraries such as Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch, ggplot2, and caret.
Hands-on experience with
PySpark
for distributed data processing and analysis.
Strong statistical and mathematical foundation in regression, classification, clustering, time-series analysis, and hypothesis testing.
Experience working with large datasets, big data platforms (Hadoop, Spark), and SQL/NoSQL databases.
Familiarity with
cloud platforms
(AWS, Azure, or GCP) and version control (Git).
Excellent problem-solving, communication, and stakeholder management skills.
Good to have Experience in
risk analytics, fraud detection, credit risk modeling, or customer analytics
in banking/financial services.