Principal Data Scientist II

Job Description

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 Masters or Bachelors 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.