Lead Data Scientist

Key Responsibilities

Leadership and Team Management

• Lead, manage, and mentor a high-performing team of data scientists, ensuring excellence in technical execution and professional development.
• Define, implement, and refine the data science strategy and roadmap, aligning with the organization's long-term goals and evolving business needs.
• Cultivate a culture of technical innovation, collaboration, and continuous learning within the data science team.
  
  

Strategic Collaboration and Communication

• Partner with senior leadership to identify and address strategic opportunities and challenges through data science initiatives.
• Present complex, data-driven insights and strategic recommendations to executive stakeholders, driving high-level decision-making.
• Develop and nurture relationships with key stakeholders across the organization, ensuring effective collaboration and alignment on data-driven projects.
  
  

Innovation and Research

• Drive the development and deployment of state-of-the-art data science methodologies, leveraging the latest advancements in machine learning, artificial intelligence, LLMs, and computational techniques.
• Conduct pioneering research in emerging areas of data science, integrating new technologies and methodologies to solve complex business problems.
• Lead the evaluation and adoption of advanced tools, technologies, and frameworks to enhance the organization's data science capabilities.
  
  

Project Management and Execution

• Oversee the execution of complex, high-impact data science projects, ensuring they meet rigorous technical standards, deadlines, and business objectives.
• Ensure the scalability, reliability, and robustness of data science solutions through advanced model development, deployment, and monitoring practices.
• Proactively manage project risks and mitigate potential issues, ensuring successful delivery and alignment with strategic goals.
  
  

Thought Leadership and Continuous Learning

• Serve as a thought leader in the data science field, representing the organization at industry events, conferences, and forums with high-level presentations and publications.
• Promote a culture of continuous learning and technical excellence within the data science team and across the organization.
• Contribute to the organization's intellectual capital through research, white papers, and industry collaborations.
  
  

Qualifications

• Ph.D., Master’s or Bachelor’s in Data Science, Computer Science, Statistics, Mathematics, or a related field.
• 10+ years of experience in data science or a related field, with a proven track record of leading and executing high-impact, technically complex projects.
• Extensive expertise in advanced machine learning algorithms, deep learning, statistical modeling, LLM, and AI, with hands-on experience in cutting-edge techniques.
• Mastery of programming languages such as Python, with extensive experience in advanced machine learning libraries and frameworks (e.g., TensorFlow, PyTorch, Keras).
• Advanced skills in SQL and hands-on experience with big data technologies.
• Demonstrated expertise in MLOps/LLMOps practices, including model deployment, monitoring, and optimization at scale.
• Exceptional problem-solving, analytical, and strategic thinking skills, with the ability to tackle the most complex data challenges.
• Proven leadership abilities, with experience managing and developing high-performing technical teams.
• Outstanding communication skills, both written and verbal, with the capability to convey complex technical concepts to diverse audiences and influence executive decision-making.
• Recognized as a thought leader in the data science community, with a strong portfolio of publications, conference presentations, or significant contributions to the field.
• Ability to manage and deliver multiple high-stakes projects simultaneously, with a deep understanding of how to align data science initiatives with overarching business objectives and strategies.
  
  

Our Commitment to a Culture of Inclusion & Belonging

Ecolab is committed to fair and equal treatment of associates and applicants and furthering the principles of Equal Opportunity to Employment. We will recruit, hire, promote, transfer and provide opportunities for advancement based on individual qualifications and job performance in all matters affecting employment, compensation, benefits, working conditions, and opportunities for advancement. Ecolab will not discriminate against any associate or applicant for employment because of race, religion, color, creed, national origin,citizenship status, sex, sexual orientation, gender identity and expressions, genetic information, marital status, age, or disability.