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Machine Learning
Python developer with Machine Learning & Banking Contract
Job Title: Python Developer Machine Learning & Banking
Location: Remote EST time zone required for working hours
Department: Auto Finance & Insurance
Duration/Type: 9 to 12 Months/Contract
Summary:
We are seeking a highly skilled Python Developer with a proven track record in developing machine learning applications within the banking and finance sector. You’ll work on cutting-edge projects that will streamline our risk assessment, fraud detection, personalized customer experiences, and financial modeling. If you have a passion for applying data-driven insights to solve complex banking challenges, this position is an outstanding opportunity.
Responsibilities
Machine Learning Model Development: Design, develop, and deploy robust
machine learning models using Python libraries such as scikit-learn, TensorFlow, or PyTorch. You will analyze banking data to identify trends, patterns, and anomalies to inform model selection.
Data Engineering & Management: Collaborate with data teams to extract,
transform, and load (ETL) relevant banking data. Ensure data integrity, implement data cleaning techniques, and optimize data pipelines for machine learning workflows.
Project Collaboration: Work eFFectively with cross-functional teams including data scientists, business analysts, and product managers to translate business
problems into eYective machine learning solutions.
Model Evaluation and Refinement: Continuously analyze the performance of
machine learning models in production. Implement strategies to improve model
accuracy, performance, and address issues like bias or overfitting.
Solution Deployment and Monitoring: Integrate machine learning models into
existing banking systems and applications. Create monitoring dashboards to track model health and detect performance degradation.
Domain Expertise: Stay updated on industry trends in machine learning
applications for banking, such as fraud detection, risk scoring, algorithmic trading, and customer behavior analysis.
Machine Learning Model Development: Design, develop, and deploy robust
machine learning models using Python libraries such as scikit-learn, TensorFlow, or PyTorch. You will analyze banking data to identify trends, patterns, and anomalies to inform model selection.
Data Engineering & Management: Collaborate with data teams to extract,
transform, and load (ETL) relevant banking data. Ensure data integrity, implement data cleaning techniques, and optimize data pipelines for machine learning workflows.
Project Collaboration: Work eFFectively with cross-functional teams including data scientists, business analysts, and product managers to translate business
problems into eYective machine learning solutions.
Model Evaluation and Refinement: Continuously analyze the performance of
machine learning models in production. Implement strategies to improve model
accuracy, performance, and address issues like bias or overfitting.
Solution Deployment and Monitoring: Integrate machine learning models into
existing banking systems and applications. Create monitoring dashboards to track model health and detect performance degradation.
Domain Expertise: Stay updated on industry trends in machine learning
applications for banking, such as fraud detection, risk scoring, algorithmic trading, and customer behavior analysis.
Qualifications
Required:
o Bachelor s degree in Computer Science, Data Science, Mathematics, or a
related field.
o Minimum of 3 years of proven Python development experience.
o Deep understanding and hands-on experience with machine learning
algorithms, techniques (supervised/unsupervised learning, classification,
regression, clustering, NLP) and their practical applications.
o Experience with data wrangling, cleaning, and feature engineering using
Python libraries (NumPy, Pandas, SciPy).
o Experience working with relational databases (SQL) and potentially NoSQL
databases.
o Demonstrable experience in the banking or financial industry, with an
understanding of domain-specific datasets and problem areas.
Preferred:
o Master’s degree in Computer Science, Data Science, or a related field.
o Experience with cloud-based machine learning platforms (AWS, Google
Cloud, Azure).
o Familiarity with big data technologies (e.g., Spark, Hadoop).
o Experience with deep learning frameworks (TensorFlow, PyTorch).
o Understanding of regulatory and compliance aspects in the banking industry.
Additional Skills
Strong Communication: Ability to clearly communicate technical concepts to non- technical stakeholders and translate business requirements into technical solutions.
Problem-Solving: Excellent analytical and problem-solving abilities applied to
machine learning challenges.
Collaborative Mindset: Thrive in a team-oriented environment and enjoy working closely with colleagues and stakeholders.
Required:
o Bachelor s degree in Computer Science, Data Science, Mathematics, or a
related field.
o Minimum of 3 years of proven Python development experience.
o Deep understanding and hands-on experience with machine learning
algorithms, techniques (supervised/unsupervised learning, classification,
regression, clustering, NLP) and their practical applications.
o Experience with data wrangling, cleaning, and feature engineering using
Python libraries (NumPy, Pandas, SciPy).
o Experience working with relational databases (SQL) and potentially NoSQL
databases.
o Demonstrable experience in the banking or financial industry, with an
understanding of domain-specific datasets and problem areas.
Preferred:
o Master’s degree in Computer Science, Data Science, or a related field.
o Experience with cloud-based machine learning platforms (AWS, Google
Cloud, Azure).
o Familiarity with big data technologies (e.g., Spark, Hadoop).
o Experience with deep learning frameworks (TensorFlow, PyTorch).
o Understanding of regulatory and compliance aspects in the banking industry.
Additional Skills
Strong Communication: Ability to clearly communicate technical concepts to non- technical stakeholders and translate business requirements into technical solutions.
Problem-Solving: Excellent analytical and problem-solving abilities applied to
machine learning challenges.
Collaborative Mindset: Thrive in a team-oriented environment and enjoy working closely with colleagues and stakeholders.
Mohan Krishna Yarramsetti
