Lead Data Scientist/ML Engineer

We are seeking a talented Lead Data Scientist/ML Engineer to join an embedded team. In this role, you will be crucial in developing and implementing machine learning solutions for several projects. Your expertise will be essential in driving the success of the company initiatives.

Work at Exadel - Who We Are

Since 1998, Exadel has been engineering its products and custom software for clients of all sizes. Headquartered in Walnut Creek, California, Exadel has 2,000+ employees in development centers across America, Europe, and Asia. People drive Exadel’s success and are at the core of our values.

Requirements

• 6+ years of experience in the Data Science/Machine Learning area
• Proven experience starting new Machine Learning projects from scratch: from problem analysis and data collection to PoC and deployment to production
• Competency in Machine Learning algorithms, their limitations, and use cases
• Knowledge of how to set up MLOps
• Experience in Generative AI, Multi-Agents systems, and Prompt Engineering
• Confidence in Python, Pandas, Scikit-learn, Matplotlib, SQL, etc.
• Expertise in ML/DL frameworks (PyTorch, TensorFlow, etc.)
• A sharp-minded person who can dive into the business domain and emerge with ideas on how to use data to make the business more effective
• Creative person who can convert the data into a story with plots and insights
• Strong communicator who can speak to a client face-to-face, understand business needs, and explain the solution in an easily digestible way

Nice to Have

• MS or BS in computer science or related field
• Familiarity with micro-service architecture, task queues (e.g., Celery), cloud (e.g., AWS or Azure), Docker, OS and networking basics, and database systems (e.g,. Postgres)
• Kaggle or GitHub account with projects that demonstrate skill level

English level

Upper-Intermediate

Responsibilities

• Understand business objectives and models that help achieve them
• Propose and realize new ideas to benefit our customers and the company
• Fully cover (develop, maintain, and monitor) the entire lifecycle of created models
• Propose new research, improvements, and best practices
• Share knowledge, ideas, and new approaches with team members
• Stay up-to-date with the latest findings in applied data science