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Senior Data Scientist
2 months ago
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 language models (LLMs).
Proven experience in leading the design, development, and deployment of AI solutions to solve complex business problems.
Proficiency in advanced statistical methods, machine learning algorithms, and deep learning techniques, along with the ability to derive insights from unstructured data.
Expertise in developing customized models, applying advanced machine learning algorithms, handling complex data sets, and leveraging ensemble learning.
Demonstrated thought leadership, innovative problem-solving abilities, and a track record of contributing to advancements in the field of data science.
Experience collaborating with various teams, stakeholders, and taking a leadership role in driving innovation and change within the organization.
Proficiency in automating workflows, optimizing processes, and identifying inefficiencies to streamline data-related tasks.
Proven ability to align data science initiatives with product goals, understand user needs, and utilize advanced analytics for product enhancement.
Proficiency in implementing continuous integration/continuous deployment methodologies and ensuring modular, well-structured, and maintainable code.
Contributions to research publications, patents, or academic achievements in the field of data science and related domains.
Understanding of AI security, compliance requirements, and ability to design solutions that meet regulatory standards.
Active engagement with the industry, such as speaking at conferences, participating in industry forums, or contributing to open-source projects.
Leadership experience in guiding teams, mentoring junior data scientists, and providing technical direction for projects.
Publications, research contributions, or teaching experience in the field of data science would be a plus.
Competencies and Behaviors
Demonstrate a high level of technical mastery in advanced statistical methods, machine learning algorithms, and deep learning concepts.
Ability to design AI solutions strategically by understanding complex business problems, evaluating algorithms, and aligning solutions with business objectives.
Drive innovation by proactively identifying new opportunities for data-driven insights and leveraging cutting-edge technologies and methodologies.
Communicate effectively with stakeholders, aligning business objectives with data science goals, and presenting insights in a clear and impactful manner.
Exhibit expertise in developing and implementing advanced machine learning models, customizing models, and leveraging ensemble learning techniques.
Display a continuous learning mindset, staying updated with emerging trends, technologies, and methodologies in the ever-evolving field of data science.
Drive process optimization by creating and modifying workflows, automating routine tasks, and suggesting solutions to improve productivity and accuracy.
Collaborate effectively with cross-functional teams, share knowledge, and contribute to a culture of teamwork and collaboration.
Align data science initiatives with the product vision, focusing on user needs, product metrics, and leveraging advanced analytics to drive product enhancements.
Adhere to AI solution design best practices, ensuring model accuracy, reliability, and compliance with ethical and security standards.
Contribute to the educational field through publications, research, or teaching activities, enhancing the collective knowledge in the domain of data science.
Leadership and Mentorship: Provide leadership by guiding teams, mentoring junior data scientists, and setting technical direction for projects, fostering a culture of growth and development.
Engage with the industry, demonstrate thought leadership, and contribute to advancements in the field, showcasing expertise and insights at conferences or forums.
Ensure efficient deployment of AI models, modularized and well-documented code, and adherence to best practices in code management and version control.
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