Principal Data Scientist 1

The Role

• This role provides an exciting opportunity to be at the forefront of AI innovation, contributing to the development and implementation of solutions that push the boundaries of technology.
• If you are passionate about AI, thrive in a collaborative environment, and enjoy driving impactful projects, this position offers a platform for creativity and professional growth. Join us in shaping the future of AI applications

Responsibilities:

• Cross-Functional Collaboration: Collaborate seamlessly with Engineering, Product, and Operations teams to conceptualize, design, and construct AI systems for diverse applications, including Email Suites, Conversational chatbots, and various NLP and ML use cases.
• Innovative Model Development: Lead the development of inventive strategies and models, ensuring practical operationalization of AI solutions in alignment with organizational goals.
• Ideation and Analysis: Generate ideas for exploratory analysis, actively shaping the trajectory of future projects. Provide insightful recommendations for strategic actions based on data-driven insights.
• Rapid Prototyping and Product Discussions: Drive the rapid prototyping of solutions, actively participating in discussions related to product and feature development.
• Dashboard Creation and Reporting: Develop dashboards and comprehensive documentation to effectively communicate results. Regularly monitor key data metrics, facilitating informed decision-making.
• Integration with Production Systems: Collaborate closely with software engineers to deploy and integrate data models into production systems. Ensure scalability, reliability, and efficiency of integrated solutions.
• Business Metrics Identification: Identify and analyze key business metrics, offering strategic insights. Recommend product features based on the identified metrics to enhance overall product functionality.

Skills

• Exceptional quantitative and problem-solving skills - capable of tackling complex data-driven challenges and formulating effective solutions.
• Solid understanding of natural language processing (NLP) - Fundamentals, including techniques such as named entity recognition (NER), topic modeling, and sentiment analysis.
• Proven proficiency - In essential data science libraries, including Pandas, Numpy, SciPy, and Scikit-Learn, for data manipulation, analysis, and modeling.
• In-depth expertise in Python programming - Focus on data analysis, model building, and algorithmic implementation.
• Experience with distributed computing frameworks - Hadoop or Spark, for handling large-scale data processing and machine learning tasks.
• Comprehensive understanding - Supervised and unsupervised machine learning techniques, including decision trees, random forests, boosting algorithms, and clustering methods.

Experience and Qualifications

• Master's or Bachelor's degree in Machine Learning/Data Science, Applied Statistics, Mathematics, or Engineering
• Minimum 8+ years of relevant experience with a proven track record of developing ML solutions.