Senior Data Scientist

Our Mission: 

6sense is on a mission to revolutionize how B2B organizations create revenue by predicting customers most likely to buy and recommending the best course of action to engage anonymous buying teams. 6sense Revenue AI is the only sales and marketing platform to unlock the ability to create, manage and convert high-quality pipeline to revenue. 

Our People: 

People are the heart and soul of 6sense. We serve with passion and purpose. We live by our Being 6sense values of Accountability, Growth Mindset, Integrity, Fun and One Team. Every 6sensor plays a part in defining the future of our industry-leading technology.  6sense is a place where difference-makers roll up their sleeves, take risks, act with integrity, and measure success by the value we create for our customers. 

We want 6sense to be the best chapter of your career. 

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 pr