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## Responsibilities
- Understand business processes, identify pain points and areas for improvement, and articulate problems that can be addressed through machine learning solutions.
- Analyze and correlate relevant business metrics to determine the impact of potential solutions.
- Gather, clean, and preprocess data from various sources to facilitate the development of machine learning models.
- Design, develop, and implement machine learning models using advanced techniques such as Neural Networks and Transformer models, with a focus on tabular data and NLP applications.
- Perform feature engineering, data preprocessing, and model selection to optimize model performance.
- Evaluate model performance using appropriate metrics, iterating and refining as necessary.
- Monitor and maintain the performance of machine learning models in production, troubleshooting and updating as required.
- Communicate findings, insights, and recommendations to stakeholders in a clear and actionable manner.
- Stay up to date with the latest research, trends, and best practices in machine learning, NLP, Neural Networks, Transformer models, and related technologies.
- Continuously improve the organization's machine learning infrastructure, tools, and processes to enhance efficiency and effectiveness.
- Proactively identify opportunities for leveraging machine learning and AI solutions to drive business value and improve operations.
- Collaborate cross-functionally with other teams, such as product development, marketing, and customer support, to ensure seamless integration and implementation of machine learning models into existing systems and workflows.
- Train and mentor junior team members, sharing knowledge and expertise in machine learning, NLP, Neural Networks, and Transformer models.
- Ensure compliance with data privacy and security regulations while handling sensitive information.
## Requirements
- Minimum Qualifications: Bachelor’s or higher degree in computer science, Data Science, Engineering, or a related field.
- Relevant years of experience in machine learning, data science, or a related field, with a focus on Neural Networks, Transformer models, tabular data, and NLP.
- Strong proficiency in Python and familiarity with machine learning libraries such as TensorFlow, PyTorch, Scikit-learn, Matplotlib, Seaborn, Plotly, Pandas, NumPy.
- Proven ability to manage projects end-to-end, from understanding business requirements to implementing machine learning solutions.
- Excellent problem-solving, critical thinking, and analytical skills.
- Strong communication and interpersonal skills, with the ability to effectively present complex information to both technical and non-technical audiences.
- Keen attention to detail and a commitment to producing high-quality, reliable, and maintainable code.
- Self-motivated and able to work independently or collaboratively within a team environment.
- A passion for staying current with the latest advancements in machine learning.
## Nice to Have
- Domain background in the finance, insurance and familiarity with related business processes and challenges.
- Proficiency in one programming language as base minimum, such as Python.
- Previous experience in a client-facing role or presenting technical information to non-technical stakeholders.
## Benefits
- Genpact is an Equal Opportunity Employer and considers applicants for all positions without regard to race, color, religion or belief, sex, age, national origin, citizenship status, marital status, military/veteran status, genetic information, sexual orientation, gender identity, physical or mental disability or any other characteristic protected by applicable laws.
- Genpact is committed to building a dynamic work environment that values diversity and inclusion, respect and integrity, customer focus, and innovation.
- For more information, visit www.genpact.com.
- Follow us on Twitter, Facebook, LinkedIn, and YouTube.
- Furthermore, please do note that Genpact does not charge fees to process job applications and applicants are not required to pay to participate in our hiring process in any other way.
- Examples of such scams include purchasing a 'starter kit,' paying to apply, or purchasing equipment or training.
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