Sr. Data Scientist

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.

Requirements:

Industry Experience: 5+ years with a bachelor's degree, or 3+ 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 in 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, and PyTorch) and libraries (scikit-learn).

CI/CD Tools: Experience with CI/CD tools and practices.

Communication: Excellent verbal and written communication skills.

Teamwork and 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 they 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).