Principal Applied Scientist

About the Role

We are looking for an experienced and vision-driven Applied Scientist to join our team. In this role, you will bridge the gap between cutting-edge scientific research and scalable, real-world product applications. You will be responsible for designing, building, and deploying advanced machine learning models that solve complex business challenges and drive meaningful impact for our users.

Key Responsibilities

• Research & Development: Research, design, and implement state-of-the-art machine learning algorithms, statistical models, and deep learning architectures.

• End-to-End Implementation: Lead the full lifecycle of scientific development—from problem formulation and data collection to prototyping, testing, deployment, and ongoing optimization.

• Technical Leadership: Serve as a technical authority by conducting rigorous code and scientific reviews, auditing experimental setups, and setting high standards for scientific excellence.

• Cross-Functional Collaboration: Partner closely with product managers, data engineers, and software engineering teams to integrate machine learning solutions into core production environments.

• Strategic Alignment: Translate high-level business goals into concrete technical roadmaps and research directions.

Basic Qualifications

• Education: Ph.D. or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related quantitative field.

• Experience: [X] years of hands-on experience developing and deploying machine learning models in production environments.

• Programming Skills: Proficiency in programming languages such as Python, C , or Java, along with deep learning frameworks (e.g., PyTorch, TensorFlow).

• Core Expertise: Deep understanding of algorithm design, model evaluation, experimental design, and data structures.

• Communication: Exceptional verbal and written communication skills with the ability to explain complex scientific concepts to non-technical stakeholders.

Preferred Qualifications

• Proven track record of publishing research in top-tier journals or conferences (e.g., NeurIPS, ICML, ACL, KDD).

• Hands-on experience with large-scale distributed systems, big data infrastructure (e.g., Spark, Hadoop), and cloud platforms (AWS, GCP, or Azure).

• Experience mentoring junior scientists and engineers or leading technical projects across cross-functional teams.

• Strong domain expertise in specific AI domains such as Natural Language Processing (NLP), Computer Vision, Reinforcement Learning, or Recommendation Systems.