Senior Data Scientist

Senior Data Scientist - M2

About Team

The Myntra Data Science team is at the forefront of innovation, delivering cutting-edge solutions that drive significant revenue and enhance customer experiences across various touchpoints. Every quarter, our models impact millions of customers, leveraging real-time, near-real-time, and offline solutions with diverse latency requirements. These models are built on massive datasets, allowing for deep learning and growth opportunities within a rapidly expanding organization. By joining our team, you'll gain hands-on experience with an extensive e-commerce platform, learning to develop models that handle millions of requests per second with sub-second latency.

We take pride in deploying solutions that not only utilize state-of-the-art machine learning techniques—such as graph neural networks, diffusion models, transformers, representation learning, optimization methods, and Bayesian modeling—but also contribute to the research community with multiple peer-reviewed publications.

Roles and Responsibilities

Design, Develop, and Deploy: Create and implement advanced machine learning models and algorithms to tackle complex business challenges across various domains, including Recommender Systems, Search, Computer Vision, Supply Chain Management (SCM), Pricing, Forecasting, Trend and Virality Prediction, Generative AI, and more.

Technical Expertise: Demonstrate deep theoretical knowledge and practical expertise in one or more areas, such as Natural Language Processing (NLP), Computer Vision, Recommender Systems, and Optimization.

Software Development: Develop robust, scalable, and maintainable software solutions for seamless model deployment.

CI/CD Pipelines: Set up and manage Continuous Integration/Continuous Deployment (CI/CD) pipelines for automated testing, deployment, and model integration.

Model Maintenance: Maintain and optimize machine learning pipelines, including data cleaning, feature extraction, and model training.

Collaboration: Work closely with the Platforms and Engineering teams to ensure smooth deployment and integration of ML models into production systems.

Data Management: Partner with the Data Platforms team to gather, process, and analyze data crucial for model development.

Code Quality: Write clean, efficient, and maintainable code following best practices.

Performance Optimization: Conduct performance testing, troubleshooting, and tuning to ensure optimal model performance.

Continuous Learning: Stay up-to-date with the latest advancements in machine learning and technology, sharing insights and knowledge across the organization.

Qualifications & Experience

• Industry Experience: 3-5+ years with a Bachelor's degree, or 2+ years with a Master’s/Ph.D. in Computer Science, Mathematics, Statistics, or a related field.
• Machine Learning Expertise: At least 2 years of hands-on experience as a Machine Learning Engineer or a similar role.
• Production Deployment: Proven experience deploying machine learning solutions into production environments.
• Programming Skills: Strong proficiency in Python or equivalent programming languages for model development.
• ML Frameworks: Familiarity with leading machine learning frameworks (Keras, TensorFlow, PyTorch) and libraries (scikit-learn).
• CI/CD Tools: Experience with CI/CD tools and practices.
• Communication: Excellent verbal and written communication skills.
• Teamwork & Independence: Ability to work collaboratively in a team environment or independently as needed.
• Workload Management: Strong organizational skills to manage and prioritize tasks, supporting your manager effectively.

Exceptional candidates are encouraged to apply, even if you don't meet every listed qualification. We're open to hiring individuals who demonstrate outstanding potential.

Nice to Have

• Research Contributions: Publications or presentations in recognized Machine Learning and Data Science journals/conferences.
• Big Data Technologies: Experience with big data technologies like Spark or other distributed computing frameworks.
• Cloud Services: Proficiency in cloud platforms (AWS, Google Cloud) and an understanding of distributed systems.
• Generative AI Exposure: Familiarity with Generative AI models.
• Database Management: Experience with SQL and/or NoSQL databases.
• ML Orchestration: Knowledge of ML orchestration tools (Airflow, Kubeflow, MLFlow).