About the Role

The Data Scientist will apply advanced analytics, machine learning, and statistical modeling to solve complex business problems. Responsibilities include data collection, cleaning, exploratory analysis, feature engineering, model development, and integrating AI/ML solutions into dashboards and applications. Collaboration with cross-functional teams is essential to ensure models align with business objectives, are reproducible, and scalable. This position supports initiatives like demand forecasting, classification, and workforce analytics, utilizing predictive modeling, NLP, and AI-driven insights to optimize operations and enhance customer and employee experiences.

Key Responsibilities

• Design, develop, and optimize machine learning, deep learning, and statistical models for complex business problems.
• Implement and integrate AI/ML models into GenAI and Agentic AI applications using technologies like LangChain, LangGraph, OpenAI APIs, RAG pipelines, and vector databases.
• Build, deploy, and maintain scalable ML pipelines from data ingestion to model monitoring and lifecycle management.
• Deploy models as APIs or services using Docker, Kubernetes, FastAPI, and cloud platforms (AWS SageMaker, GCP Vertex AI, Azure ML), implementing CI/CD for automation.
• Monitor model performance in production, detect drift, and establish alerting mechanisms for reliability, compliance, and governance.
• Embed models into real-time dashboards and analytics applications to provide actionable insights to business and HR stakeholders.
• Ensure model explainability (XAI), security, audit logging, and adherence to ethical, regulatory, and enterprise standards.
• Conduct and participate in training and workshops to share AI/ML techniques and best practices.
• Translate complex model results into clear, actionable recommendations for business and non-technical stakeholders.
• Create documentation, reproducible workflows, and explanatory reports to support model adoption and team learning.
• Collaborate with senior data scientists, engineers, and product teams to implement AI/ML solutions.
• Execute tasks such as data cleaning, exploratory analysis, feature engineering, and baseline model development under guidance.
• Assist in presenting findings, visualizations, and insights to stakeholders, translating technical results into actionable recommendations.

Qualifications

Minimum Qualifications

• Bachelor’s or Master’s degree in data science, Computer Science, Statistics, Mathematics, or a related quantitative field.
• Significant hands-on experience in data analysis, machine learning, or AI projects.
• Certification in areas like GenAI, Databricks is a plus.

Preferred Qualifications

• Hands-on experience in data preprocessing, exploratory data analysis, and feature engineering.
• Knowledge of machine learning, deep learning, and NLP techniques.
• Proficiency in Python, SQL, ML/DL frameworks (TensorFlow, PyTorch, Scikit-learn).
• Familiarity with cloud platforms (AWS, Azure, GCP) and data visualization tools.
• Experience integrating models into dashboards, analytics tools, and AI applications.
• Familiarity with MLOps practices, model monitoring, CI/CD workflows, and cloud platforms.
• Ability to translate business problems into data-driven solutions and communicate insights effectively.

Why Genpact?

• Be a transformation leader at the cutting edge of AI, automation, and digital innovation.
• Make an impact by driving change for global enterprises and solving critical business challenges.
• Accelerate your career through hands-on experience, mentorship, and continuous learning.
• Join a team of 140,000+ bold thinkers and problem-solvers.
• Thrive in a values-driven culture built on courage, curiosity, integrity, and inclusion.