Senior Data Scientist | 6sense | $156k – $228k | Remote (USA)

Purpose of the Job Sr Data Scientist leads and drives strategic AI solutions, leveraging advanced data science expertise and innovative problem-solving skills. As a Senior Data Scientist, the role involves designing complex AI solutions aligned with business objectives, utilizing a deep understanding of cutting-edge algorithms and methodologies. This position focuses on continuous learning and adaptation to emerging technologies, ensuring the highest level of technical mastery. Additionally, the role emphasizes collaboration, mentorship, and thought leadership to contribute to the organization’s growth and maintain a standard of excellence in AI solution design and deployment. Responsibilities & Accountabilities • AI Solution Design and Development: Lead the design and development of AI solutions, identifying complex business problems and developing high-level architectures with minimal guidance. • Evaluate various algorithms and data sets to determine the most effective solutions for given business problems, ensuring optimal model performance. • Handle AI solution requirement gathering, design, and development process management to meet business objectives and stakeholder needs. • Effectively communicate insights, recommendations, and AI solution progress to stakeholders, ensuring alignment with business goals. • Develop customized models tailored to specific business problems using advanced machine learning algorithms and handling complex and unstructured data sets. • Proactively identify new opportunities for data-driven insights and innovations, staying updated with emerging trends and methodologies in data science. • Develop and implement complex AI models, leverage transfer learning, ensemble methods, and integrate AI solutions into complex systems or workflows. • Create and modify workflows, automate routine tasks, identify inefficiencies, and suggest solutions to streamline processes for improved productivity. • Utilize advanced analytics techniques to extract insights, develop data-driven strategies, and align business objectives with innovative data science solutions. • Maintain expertise in cutting-edge technologies, continuously develop skills in advanced data manipulation, AI development frameworks, and data analysis tools. • Prioritize business objectives, deeply understand target users, create user personas, measure product success, and communicate insights to both technical and non-technical stakeholders. • Implement continuous integration/continuous deployment methodologies, manage code modularization, version control, and maintain well-documented, maintainable code. • Collaborate with other developers, share expertise, and align business goals with data science initiatives through effective teamwork and knowledge sharing. • Apply best practices for AI solution design, testing, validation, and adhere to ethical guidelines while ensuring the accuracy, consistency, and reliability of AI models and solutions. Performance Measurement • Solution Effectiveness: Measure the effectiveness and impact of AI solutions in addressing complex business problems and achieving set objectives. • Model Performance and Innovation: Assess the performance and innovation of AI models developed, considering advancements in algorithms, techniques, and their impact on problem-solving. • Stakeholder Satisfaction: Evaluate stakeholder satisfaction and feedback regarding AI solution communication, alignment with business goals, and meeting expectations. • Process Efficiency and Automation Impact: Measure the impact of process optimizations, workflow automation, and suggestions for efficiency improvements in data-related tasks. • Product Alignment and User Impact: Evaluate how data-driven insights and solutions align with the product vision, user needs, and positively impact product development and user experience. • Continuous Learning and Skills Development: Assess the commitment to continuous learning, skill development, and staying updated with emerging trends and technologies in the data science field. • Collaboration and Knowledge Sharing: Measure collaboration effectiveness, knowledge sharing, and the ability to work within a team to integrate AI solutions into complex systems or workflows. • Quality Assurance and Compliance: Evaluate adherence to AI solution design best practices, ethical guidelines, and compliance with security and regulatory requirements. • Efficient Deployment and Code Management: Assess efficiency in deploying AI models, effective use of CI/CD methodologies, and maintaining modular, well-documented, and maintainable code. Educational and Experience Requirements • Ph.D. or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field. • Minimum of 5-7 years of hands-on experience in data analysis, machine learning model development, AI solution design, and deployment. • Proficient in Python, SQL, Pytorch or Tensorflow. • Hands on experience with NLP and large